Instructions to use shreyess/gradient_slerp_tongyi_base_attn_0.8_mlp_0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shreyess/gradient_slerp_tongyi_base_attn_0.8_mlp_0.2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="shreyess/gradient_slerp_tongyi_base_attn_0.8_mlp_0.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shreyess/gradient_slerp_tongyi_base_attn_0.8_mlp_0.2")
model = AutoModelForCausalLM.from_pretrained("shreyess/gradient_slerp_tongyi_base_attn_0.8_mlp_0.2", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use shreyess/gradient_slerp_tongyi_base_attn_0.8_mlp_0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shreyess/gradient_slerp_tongyi_base_attn_0.8_mlp_0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shreyess/gradient_slerp_tongyi_base_attn_0.8_mlp_0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shreyess/gradient_slerp_tongyi_base_attn_0.8_mlp_0.2

SGLang

How to use shreyess/gradient_slerp_tongyi_base_attn_0.8_mlp_0.2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "shreyess/gradient_slerp_tongyi_base_attn_0.8_mlp_0.2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shreyess/gradient_slerp_tongyi_base_attn_0.8_mlp_0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "shreyess/gradient_slerp_tongyi_base_attn_0.8_mlp_0.2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shreyess/gradient_slerp_tongyi_base_attn_0.8_mlp_0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use shreyess/gradient_slerp_tongyi_base_attn_0.8_mlp_0.2 with Docker Model Runner:
```
docker model run hf.co/shreyess/gradient_slerp_tongyi_base_attn_0.8_mlp_0.2
```

gradient_slerp_tongyi_base_attn_0.6_mlp_0.4

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

/home/fractal_admin/shreyas/Reasoning/models/FV-30B-A3B
/home/fractal_admin/shreyas/Reasoning/models/tongyi-deepresearch

Configuration

The following YAML configuration was used to produce this model:


# models:
#   - model: /home/fractal_admin/shreyas/Reasoning/models/FV-30B-A3B
#   - model: /home/fractal_admin/shreyas/Reasoning/models/tongyi-deepresearch
# merge_method: slerp
# base_model: /home/fractal_admin/shreyas/Reasoning/models/FV-30B-A3B
# parameters:
#   t:
#     - filter: self_attn
#       value: [0, 0.5, 0.3, 0.7, 1]
#     - filter: mlp
#       value: [1, 0.5, 0.7, 0.3, 0]
#     - value: 0.5 # fallback for rest of tensors
# dtype: float16

models:
      - model: /home/fractal_admin/shreyas/Reasoning/models/FV-30B-A3B
      - model: /home/fractal_admin/shreyas/Reasoning/models/tongyi-deepresearch

merge_method: slerp
base_model: /home/fractal_admin/shreyas/Reasoning/models/tongyi-deepresearch
parameters:
  t:
    # Attention = keep more DeepResearch (reasoning patterns live here)
    - filter: self_attn
      value: 0.8

    # MLP = keep more HealthBench (knowledge + domain tuning)
    - filter: mlp
      value: 0.2

    # fallback
    - value: 0.5

dtype: bfloat16


# mergekit-yaml \
# /home/fractal_admin/shreyas/Reasoning/mergekit/examples/gradient-slerp.yml \
# /home/fractal_admin/shreyas/Reasoning/mergekit/merges/gradient_slerp_tongyi_base_attn_0.6_mlp_0.4 \
# --cuda --lazy-unpickle \
# --allow-crimes

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Safetensors

Model size

31B params

Tensor type

BF16