metadata
language:
- sv
- en
- 'no'
- da
license: mit
tags:
- pretrained
- flashback
- web
- conversational
- TensorBlock
- GGUF
models:
- timpal0l/Mistral-7B-v0.1-flashback-v2-instruct
pipeline_tag: text-generation
widget:
- text: Jag tycker att det är roligt med
base_model: timpal0l/Mistral-7B-v0.1-flashback-v2
model-index:
- name: Mistral-7B-v0.1-flashback-v2
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 57.17
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=timpal0l/Mistral-7B-v0.1-flashback-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 80.74
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=timpal0l/Mistral-7B-v0.1-flashback-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 59.98
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=timpal0l/Mistral-7B-v0.1-flashback-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 40.66
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=timpal0l/Mistral-7B-v0.1-flashback-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 77.19
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=timpal0l/Mistral-7B-v0.1-flashback-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 29.42
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=timpal0l/Mistral-7B-v0.1-flashback-v2
name: Open LLM Leaderboard
timpal0l/Mistral-7B-v0.1-flashback-v2 - GGUF
This repo contains GGUF format model files for timpal0l/Mistral-7B-v0.1-flashback-v2.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b5165.
Our projects
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| An OpenAI-compatible multi-provider routing layer. | |
| 🚀 Try it now! 🚀 | |
| Awesome MCP Servers | TensorBlock Studio |
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| A comprehensive collection of Model Context Protocol (MCP) servers. | A lightweight, open, and extensible multi-LLM interaction studio. |
| 👀 See what we built 👀 | 👀 See what we built 👀 |
Prompt template
<s>[INST] <<SYS>>
{system_prompt}
<</SYS>>
{prompt} [/INST]
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| Mistral-7B-v0.1-flashback-v2-Q2_K.gguf | Q2_K | 2.719 GB | smallest, significant quality loss - not recommended for most purposes |
| Mistral-7B-v0.1-flashback-v2-Q3_K_S.gguf | Q3_K_S | 3.165 GB | very small, high quality loss |
| Mistral-7B-v0.1-flashback-v2-Q3_K_M.gguf | Q3_K_M | 3.519 GB | very small, high quality loss |
| Mistral-7B-v0.1-flashback-v2-Q3_K_L.gguf | Q3_K_L | 3.822 GB | small, substantial quality loss |
| Mistral-7B-v0.1-flashback-v2-Q4_0.gguf | Q4_0 | 4.109 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| Mistral-7B-v0.1-flashback-v2-Q4_K_S.gguf | Q4_K_S | 4.140 GB | small, greater quality loss |
| Mistral-7B-v0.1-flashback-v2-Q4_K_M.gguf | Q4_K_M | 4.368 GB | medium, balanced quality - recommended |
| Mistral-7B-v0.1-flashback-v2-Q5_0.gguf | Q5_0 | 4.998 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| Mistral-7B-v0.1-flashback-v2-Q5_K_S.gguf | Q5_K_S | 4.998 GB | large, low quality loss - recommended |
| Mistral-7B-v0.1-flashback-v2-Q5_K_M.gguf | Q5_K_M | 5.131 GB | large, very low quality loss - recommended |
| Mistral-7B-v0.1-flashback-v2-Q6_K.gguf | Q6_K | 5.942 GB | very large, extremely low quality loss |
| Mistral-7B-v0.1-flashback-v2-Q8_0.gguf | Q8_0 | 7.696 GB | very large, extremely low quality loss - not recommended |
Downloading instruction
Command line
Firstly, install Huggingface Client
pip install -U "huggingface_hub[cli]"
Then, downoad the individual model file the a local directory
huggingface-cli download tensorblock/timpal0l_Mistral-7B-v0.1-flashback-v2-GGUF --include "Mistral-7B-v0.1-flashback-v2-Q2_K.gguf" --local-dir MY_LOCAL_DIR
If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:
huggingface-cli download tensorblock/timpal0l_Mistral-7B-v0.1-flashback-v2-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'