EdgeRunner-Tactical-7B
Introduction
EdgeRunner-Tactical-7B is a powerful and efficient language model for the edge. Our mission is to build Generative AI for the edge that is safe, secure, and transparent. To that end, the EdgeRunner team is proud to release EdgeRunner-Tactical-7B, the most powerful language model for its size to date.
EdgeRunner-Tactical-7B is a 7 billion parameter language model that delivers powerful performance while demonstrating the potential of running state-of-the-art (SOTA) models at the edge.
Highlights
- 7 billion parameters that balance power and efficiency
- SOTA performance within the 7B model range
- Initialized from Qwen2-Instruct, leveraging prior advancements
- Self-Play Preference Optimization (SPPO) applied for continuous training and alignment
- Competitive performance on several benchmarks with Meta’s Llama-3-70B, Mixtral 8x7B, and Yi 34B
- Context length of 128K tokens, ideal for extensive conversations and large-scale text tasks
Quickstart
Below is a code snippet to show you how to load the tokenizer and model, and how to generate contents.
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"edgerunner-ai/EdgeRunner-Tactical-7B",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("edgerunner-ai/EdgeRunner-Tactical-7B")
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
Example Outputs
Create a Quantum Future:
Ask for a structured JSON output:
Evaluation
In this section, we report the results for EdgeRunner-Tactical-7B models on standard automatic benchmarks. Below are the results.
Arena-Hard Benchmark
| Model | Score | CI | Avg Tokens |
|---|---|---|---|
| gpt-4-turbo-2024-04-09 | 82.63 | (-1.71, +1.57) | 662.0 |
| claude-3-5-sonnet-20240620 | 79.35 | (-1.45, +2.06) | 567.0 |
| gpt-4o-2024-05-13 | 79.21 | (-1.50, +1.66) | 696.0 |
| gpt-4-0125-preview | 77.96 | (-2.12, +1.63) | 619.0 |
| gpt-4o-mini | 74.94 | (-2.40, +1.75) | 668.0 |
| gemini-1.5-pro-api-0514 | 71.96 | (-2.39, +2.10) | 676.0 |
| yi-large-preview | 71.48 | (-2.03, +3.14) | 720.0 |
| glm-4-0520 | 63.84 | (-2.72, +1.81) | 636.0 |
| yi-large | 63.7 | (-2.72, +2.21) | 626.0 |
| deepseek-coder-v2 | 62.3 | (-1.73, +2.41) | 578.0 |
| claude-3-opus-20240229 | 60.36 | (-2.84, +2.75) | 541.0 |
| gemma-2-27b-it | 57.51 | (-2.35, +2.46) | 577.0 |
| glm-4-0116 | 55.72 | (-2.51, +2.31) | 622.0 |
| gemini-1.5-pro-api-0409-preview | 53.37 | (-2.53, +1.89) | 478.0 |
| glm-4-air | 50.88 | (-2.60, +2.45) | 619.0 |
| gpt-4-0314 | 50.0 | (-0.00, +0.00) | 423.0 |
| gemini-1.5-flash-api-0514 | 49.61 | (-2.93, +2.85) | 642.0 |
| qwen2-72b-instruct | 46.86 | (-2.51, +2.22) | 515.0 |
| claude-3-sonnet-20240229 | 46.8 | (-2.94, +2.35) | 552.0 |
| llama-3-70b-instruct | 46.57 | (-2.00, +2.66) | 591.0 |
| claude-3-haiku-20240307 | 41.47 | (-2.15, +2.65) | 505.0 |
| gpt-4-0613 | 37.9 | (-2.21, +2.51) | 354.0 |
| mistral-large-2402 | 37.71 | (-1.88, +2.77) | 400.0 |
| EdgeRunner-Tactical-7B | 37.47 | (-2.74, +2.57) | 721.0 |
| mixtral-8x22b-instruct-v0.1 | 36.36 | (-2.61, +2.60) | 430.0 |
| qwen1.5-72b-chat | 36.12 | (-2.81, +2.39) | 474.0 |
| phi-3-medium-4k-instruct | 33.37 | (-2.02, +2.25) | 517.0 |
| mistral-medium | 31.9 | (-2.54, +2.13) | 485.0 |
| phi-3-small-8k-instruct | 29.77 | (-2.16, +2.02) | 568.0 |
| mistral-next | 27.37 | (-1.90, +1.99) | 297.0 |
| qwen2-7b-instruct | 25.2 | (-1.55, +2.46) | 618.0 |
| gpt-3.5-turbo-0613 | 24.82 | (-2.15, +1.90) | 401.0 |
| claude-2.0 | 23.99 | (-1.90, +1.75) | 295.0 |
| Arcee-Spark | 23.52 | (-2.03, +1.73) | 622.0 |
| mixtral-8x7b-instruct-v0.1 | 23.4 | (-1.87, +1.73) | 457.0 |
| gpt-3.5-turbo-0125 | 23.34 | (-1.46, +2.31) | 329.0 |
| yi-34b-chat | 23.15 | (-2.15, +1.85) | 611.0 |
| starling-lm-7b-beta | 23.01 | (-1.98, +1.71) | 530.0 |
| claude-2.1 | 22.77 | (-1.48, +2.38) | 290.0 |
| llama-3-8b-instruct | 20.56 | (-1.65, +2.09) | 585.0 |
| gpt-3.5-turbo-1106 | 18.87 | (-1.79, +2.34) | 285.0 |
| gpt-3.5-turbo-0314 | 18.05 | (-1.47, +2.09) | 334.0 |
| gemini-pro | 17.8 | (-1.65, +1.54) | 322.0 |
| phi-3-mini-128k-instruct | 15.43 | (-1.71, +1.60) | 609.0 |
| mistral-7b-instruct | 12.57 | (-1.58, +1.54) | 541.0 |
| gemma-1.1-7b-it | 12.09 | (-1.35, +1.56) | 341.0 |
| llama-2-70b-chat | 11.55 | (-1.18, +1.27) | 595.0 |
| vicuna-33b | 8.63 | (-0.88, +1.28) | 451.0 |
| gemma-7b-it | 7.47 | (-1.05, +1.09) | 378.0 |
| gemma-1.1-2b-it | 3.37 | (-0.67, +0.70) | 316.0 |
| gemma-2b-it | 3.0 | (-0.68, +0.62) | 369.0 |
InfiniteBench
| Task Name | GPT-4 | YaRN-Mistral-7B | Kimi-Chat | Claude 2 | Yi-6B-200K | Yi-34B-200K | Chatglm3-6B-128K | EdgeRunner-Tactical-7B | Qwen2-7B-Instruct |
|---|---|---|---|---|---|---|---|---|---|
| Retrieve.PassKey | 100% | 92.71% | 98.14% | 97.80% | 100.00% | 100.00% | 92.20% | 100% | 100% |
| Retrieve.Number | 100% | 56.61% | 95.42% | 98.14% | 94.92% | 100.00% | 80.68% | 100% | 99.83% |
| Retrieve.KV | 89.00% | < 5% | 53.60% | 65.40% | < 5% | < 5% | < 5% | 2.2% | 1.8% |
| En.Sum | 14.73% | 9.09% | 17.96% | 14.50% | < 5% | < 5% | < 5% | 33.07% | 29.13% |
| En.QA | 22.44% | 9.55% | 16.52% | 11.97% | 9.20% | 12.17% | < 5% | 3.4% | 9.09% |
| En.MC | 67.25% | 27.95% | 72.49% | 62.88% | 36.68% | 38.43% | 10.48% | 66.81% | 66.37% |
| En.Dia | 8.50% | 7.50% | 11.50% | 46.50% | < 5% | < 5% | < 5% | 29% | 17% |
| Zh.QA | 25.96% | 16.98% | 17.93% | 9.64% | 15.07% | 13.61% | < 5% | 4.6% | 11.14% |
| Code.Debug | 37.06% | < 5% | 17.77% | < 5% | 9.14% | 13.96% | 7.36% | 22.08% | 24.61% |
| Code.Run | 23.25% | < 5% | < 5% | < 5% | < 5% | < 5% | < 5% | 0% | 0.5% |
| Math.Calc | < 5% | < 5% | < 5% | < 5% | < 5% | < 5% | < 5% | 0% | 0% |
| Math.Find | 60.00% | 17.14% | 12.57% | 32.29% | < 5% | 25.71% | 7.71% | 29.14% | 31.42% |
GSM@ZeroEval
| Model | Acc | No answer | Reason Lens |
|---|---|---|---|
| Llama-3.1-405B-Instruct-Turbo | 95.91 | 0.08 | 365.07 |
| claude-3-5-sonnet-20240620 | 95.6 | 0 | 465.19 |
| claude-3-opus-20240229 | 95.6 | 0 | 410.62 |
| gpt-4o-2024-05-13 | 95.38 | 0 | 479.98 |
| gpt-4o-mini-2024-07-18 | 94.24 | 0 | 463.71 |
| deepseek-chat | 93.93 | 0 | 495.52 |
| deepseek-coder | 93.78 | 0 | 566.89 |
| gemini-1.5-pro | 93.4 | 0 | 389.17 |
| Meta-Llama-3-70B-Instruct | 93.03 | 0 | 352.05 |
| Qwen2-72B-Instruct | 92.65 | 0 | 375.96 |
| claude-3-sonnet-20240229 | 91.51 | 0 | 762.69 |
| gemini-1.5-flash | 91.36 | 0 | 344.61 |
| gemma-2-27b-it@together | 90.22 | 0 | 364.68 |
| claude-3-haiku-20240307 | 88.78 | 0 | 587.65 |
| gemma-2-9b-it | 87.41 | 0 | 394.83 |
| reka-core-20240501 | 87.41 | 0.08 | 414.7 |
| Athene-70B | 86.66 | 0.3 | 253.53 |
| Yi-1.5-34B-Chat | 84.08 | 0.08 | 553.47 |
| Llama-3.1-8B-Instruct | 82.87 | 0.45 | 414.19 |
| Mistral-Nemo-Instruct-2407 | 82.79 | 0 | 349.81 |
| yi-large-preview | 82.64 | 0 | 514.25 |
| EdgeRunner-Tactical-7B | 81.12 | 0.08 | 615.89 |
| gpt-3.5-turbo-0125 | 80.36 | 0 | 350.97 |
| command-r-plus | 80.14 | 0.08 | 294.08 |
| Qwen2-7B-Instruct | 80.06 | 0 | 452.6 |
| yi-large | 80.06 | 0 | 479.87 |
| Yi-1.5-9B-Chat | 76.42 | 0.08 | 485.39 |
| Phi-3-mini-4k-instruct | 75.51 | 0 | 462.53 |
| reka-flash-20240226 | 74.68 | 0.45 | 460.06 |
| Mixtral-8x7B-Instruct-v0.1 | 70.13 | 2.27 | 361.12 |
| command-r | 52.99 | 0 | 294.43 |
| Qwen2-1.5B-Instruct | 43.37 | 4.78 | 301.67 |
MMLU-REDUX@ZeroEval
| Model | Acc | No answer | Reason Lens |
|---|---|---|---|
| gpt-4o-2024-05-13 | 88.01 | 0.14 | 629.79 |
| claude-3-5-sonnet-20240620 | 86 | 0.18 | 907.1 |
| Llama-3.1-405B-Instruct-Turbo | 85.64 | 0.76 | 449.71 |
| gpt-4-turbo-2024-04-09 | 85.31 | 0.04 | 631.38 |
| gemini-1.5-pro | 82.76 | 1.94 | 666.7 |
| claude-3-opus-20240229 | 82.54 | 0.58 | 500.35 |
| yi-large-preview | 82.15 | 0.14 | 982.6 |
| gpt-4-0314 | 81.64 | 0.04 | 397.22 |
| Qwen2-72B-Instruct | 81.61 | 0.29 | 486.41 |
| gpt-4o-mini-2024-07-18 | 81.5 | 0.07 | 526 |
| yi-large | 81.17 | 0 | 774.85 |
| deepseek-chat | 80.81 | 0.11 | 691.91 |
| deepseek-coder | 79.63 | 0.14 | 704.72 |
| Meta-Llama-3-70B-Instruct | 78.01 | 0.11 | 520.77 |
| gemini-1.5-flash | 77.36 | 1.26 | 583.45 |
| Athene-70B | 76.64 | 0.04 | 552.61 |
| reka-core-20240501 | 76.42 | 0.76 | 701.67 |
| gemma-2-27b-it@together | 75.67 | 0.61 | 446.51 |
| claude-3-sonnet-20240229 | 74.87 | 0.07 | 671.75 |
| gemma-2-9b-it@nvidia | 72.82 | 0.76 | 499 |
| Yi-1.5-34B-Chat | 72.79 | 1.01 | 620.1 |
| claude-3-haiku-20240307 | 72.32 | 0.04 | 644.59 |
| Phi-3-mini-4k-instruct | 70.34 | 0.43 | 677.09 |
| command-r-plus | 68.61 | 0 | 401.51 |
| gpt-3.5-turbo-0125 | 68.36 | 0.04 | 357.92 |
| EdgeRunner-Tactical-7B | 67.71 | 0.65 | 917.6 |
| Llama-3.1-8B-Instruct | 67.13 | 3.38 | 399.54 |
| Qwen2-7B-Instruct | 66.92 | 0.72 | 533.15 |
| Mistral-Nemo-Instruct-2407 | 66.88 | 0.47 | 464.19 |
| Yi-1.5-9B-Chat | 65.05 | 4.61 | 542.87 |
| reka-flash-20240226 | 64.72 | 0.32 | 659.25 |
| Mixtral-8x7B-Instruct-v0.1 | 63.17 | 5.51 | 324.31 |
| Meta-Llama-3-8B-Instruct | 61.66 | 0.97 | 600.81 |
| command-r | 61.12 | 0.04 | 382.23 |
| Qwen2-1.5B-Instruct | 41.11 | 7.74 | 280.56 |
WildBench
| Model | WB_Elo | RewardScore_Avg | task_macro_reward.K=-1 | Length |
|---|---|---|---|---|
| gpt-4o-2024-05-13 | 1248.12 | 50.05 | 40.80 | 3723.52 |
| claude-3-5-sonnet-20240620 | 1229.76 | 46.16 | 37.63 | 2911.85 |
| gpt-4-turbo-2024-04-09 | 1225.29 | 46.19 | 37.17 | 3093.17 |
| gpt-4-0125-preview | 1211.44 | 41.24 | 30.20 | 3335.64 |
| gemini-1.5-pro | 1209.23 | 45.27 | 37.59 | 3247.97 |
| yi-large-preview | 1209.00 | 46.92 | 38.54 | 3512.68 |
| claude-3-opus-20240229 | 1206.56 | 37.03 | 22.35 | 2685.98 |
| Meta-Llama-3-70B-Instruct | 1197.72 | 35.15 | 22.54 | 3046.64 |
| Athene-70B | 1197.41 | 29.77 | 0.00 | 3175.14 |
| deepseek-coder-v2 | 1194.11 | 29.39 | 11.38 | 2795.31 |
| gpt-4o-mini-2024-07-18 | 1192.43 | 28.57 | 0.00 | 3648.13 |
| yi-large | 1191.88 | 33.35 | 17.77 | 3095.34 |
| gemini-1.5-flash | 1190.30 | 37.45 | 26.04 | 3654.40 |
| deepseek-v2-chat-0628 | 1188.07 | 27.00 | 0.00 | 3252.38 |
| gemma-2-9b-it-SimPO | 1184.67 | 26.64 | 0.00 | 4277.67 |
| gemma-2-9b-it-DPO | 1182.43 | 26.61 | 0.00 | 3982.63 |
| nemotron-4-340b-instruct | 1181.77 | 33.76 | 19.85 | 2754.01 |
| claude-3-sonnet-20240229 | 1179.81 | 28.09 | 10.70 | 2670.24 |
| deepseekv2-chat | 1178.76 | 30.41 | 12.60 | 2896.97 |
| gemma-2-27b-it@together | 1178.34 | 24.27 | 0.00 | 2924.55 |
| Qwen2-72B-Instruct | 1176.75 | 24.77 | 5.03 | 2856.45 |
| reka-core-20240501 | 1173.85 | 31.48 | 17.06 | 2592.59 |
| Mistral-Nemo-Instruct-2407 | 1165.29 | 22.19 | 0.00 | 3318.21 |
| Yi-1.5-34B-Chat | 1163.69 | 30.83 | 16.06 | 3523.56 |
| EdgeRunner-Tactical-7B | 1162.88 | 22.26 | 0.00 | 3754.66 |
| claude-3-haiku-20240307 | 1160.56 | 16.30 | -6.30 | 2601.03 |
| mistral-large-2402 | 1159.72 | 13.27 | -12.36 | 2514.98 |
| deepseek-v2-coder-0628 | 1155.97 | 22.83 | 0.00 | 2580.18 |
| gemma-2-9b-it | 1154.30 | 21.35 | 0.00 | 2802.89 |
| command-r-plus | 1153.15 | 16.58 | -3.60 | 3293.81 |
| glm-4-9b-chat | 1152.68 | 20.71 | 2.33 | 3692.04 |
| Qwen1.5-72B-Chat-greedy | 1151.97 | 20.83 | 1.72 | 2392.36 |
| Yi-1.5-9B-Chat | 1151.43 | 21.80 | 4.93 | 3468.23 |
| Meta-Llama-3-8B-Instruct | 1140.76 | 6.72 | -15.76 | 2975.19 |
| Qwen2-7B-Instruct | 1137.66 | 16.20 | 0.00 | 3216.43 |
| Starling-LM-7B-beta-ExPO | 1137.58 | 11.28 | -9.01 | 2835.83 |
| Hermes-2-Theta-Llama-3-8B | 1135.99 | 3.18 | -23.28 | 2742.17 |
| Llama-3.1-8B-Instruct | 1135.42 | 16.38 | 0.00 | 3750.60 |
AlpacaEval 2.0
| Model | Length Controlled Winrate | Win Rate | N Total | Avg Length |
|---|---|---|---|---|
| gpt-4o-2024-05-13 | 57.46 | 51.33 | 805 | 1873 |
| gpt-4-turbo-2024-04-09 | 55.02 | 46.12 | 805 | 1802 |
| claude-3-5-sonnet-20240620 | 52.37 | 40.56 | 805 | 1488 |
| yi-large-preview | 51.89 | 57.47 | 805 | 2335 |
| gpt4_1106_preview | 50.0 | 50.0 | 805 | 2049 |
| Qwen1.5-110B-Chat | 43.91 | 33.78 | 805 | 1631 |
| claude-3-opus-20240229 | 40.51 | 29.11 | 805 | 1388 |
| gpt4 | 38.13 | 23.58 | 805 | 1365 |
| Qwen1.5-72B-Chat | 36.57 | 26.5 | 805 | 1549 |
| gpt4_0314 | 35.31 | 22.07 | 805 | 1371 |
| Meta-Llama-3-70B-Instruct | 34.42 | 33.18 | 805 | 1919 |
| EdgeRunner-Tactical-7B | 34.41 | 51.28 | 805 | 2735 |
| mistral-large-2402 | 32.65 | 21.44 | 805 | 1362 |
| Mixtral-8x22B-Instruct-v0.1 | 30.88 | 22.21 | 805 | 1445 |
| gpt4_0613 | 30.18 | 15.76 | 805 | 1140 |
| mistral-medium | 28.61 | 21.86 | 805 | 1500 |
| claude-2 | 28.16 | 17.19 | 805 | 1069 |
| Samba-CoE-v0.2 | 27.62 | 21.85 | 805 | 1469 |
| internlm2-chat-20b-ExPO | 27.23 | 46.19 | 805 | 3335 |
| Yi-34B-Chat | 27.19 | 29.66 | 805 | 2123 |
| Starling-LM-7B-beta-ExPO | 26.41 | 29.6 | 805 | 2215 |
| Llama-3.1-8B-Instruct | 26.41 | 30.32 | 805 | 2171 |
| Snorkel-Mistral-PairRM-DPO | 26.39 | 30.22 | 804 | 2736 |
| Arcee-Spark | 25.58 | 26.19 | 805 | 2002 |
| claude-2.1 | 25.25 | 15.73 | 805 | 1096 |
| gemini-pro | 24.38 | 18.18 | 805 | 1456 |
| Qwen1.5-14B-Chat | 23.9 | 18.65 | 805 | 1607 |
| Mixtral-8x7B-Instruct-v0.1 | 23.69 | 18.26 | 805 | 1465 |
| Meta-Llama-3-8B-Instruct | 22.92 | 22.57 | 805 | 1899 |
| Samba-CoE-v0.1 | 22.87 | 16.84 | 805 | 1316 |
| gpt-3.5-turbo-0613 | 22.35 | 14.1 | 805 | 1331 |
| Qwen2-7B-Instruct | 21.51 | 18.93 | 805 | 1793 |
| gpt-3.5-turbo-1106 | 19.3 | 9.18 | 805 | 796 |
| internlm2-chat-20b-ppo | 18.75 | 21.75 | 805 | 2373 |
| claude-2.1_concise | 18.21 | 9.23 | 805 | 573 |
| gpt-3.5-turbo-0301 | 18.09 | 9.62 | 805 | 827 |
| deepseek-llm-67b-chat | 17.84 | 12.09 | 805 | 1151 |
| vicuna-33b-v1.3 | 17.57 | 12.71 | 805 | 1479 |
| Mistral-7B-Instruct-v0.2 | 17.11 | 14.72 | 805 | 1676 |
| OpenHermes-2.5-Mistral-7B | 16.25 | 10.34 | 805 | 1107 |
| Qwen1.5-7B-Chat | 14.75 | 11.77 | 805 | 1594 |
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