Instructions to use chargoddard/mistral-11b-slimorca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chargoddard/mistral-11b-slimorca with Transformers:

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

pipe = pipeline("text-generation", model="chargoddard/mistral-11b-slimorca")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("chargoddard/mistral-11b-slimorca")
model = AutoModelForCausalLM.from_pretrained("chargoddard/mistral-11b-slimorca")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use chargoddard/mistral-11b-slimorca with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chargoddard/mistral-11b-slimorca"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chargoddard/mistral-11b-slimorca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/chargoddard/mistral-11b-slimorca

SGLang

How to use chargoddard/mistral-11b-slimorca 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 "chargoddard/mistral-11b-slimorca" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chargoddard/mistral-11b-slimorca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "chargoddard/mistral-11b-slimorca" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chargoddard/mistral-11b-slimorca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use chargoddard/mistral-11b-slimorca with Docker Model Runner:
```
docker model run hf.co/chargoddard/mistral-11b-slimorca
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Full weight fine tuned on two epochs of SlimOrca. Uses Mistral Instruct's prompt format.

The base model for this came from a variation on Undi's Mistral 11B recipe. The o_proj and down_proj tensors were set to zero in the added layers, making the output exactly identical to Mistral 7B before training.

~~Benchmarks look good locally but still evaluating actual usefulness.~~ Update: this turned out great! 10/10 would recommend as a training approach.

Reproducing

This mergekit config was used to produce the base model:

slices:
  - sources:
      - model: mistralai/Mistral-7B-v0.1
        layer_range: [0, 24]
  - sources: # add middle layers with residuals scaled to zero
      - model: mistralai/Mistral-7B-v0.1
        layer_range: [8, 24]
        parameters:
          scale:
            - filter: o_proj
              value: 0.0
            - filter: down_proj
              value: 0.0
            - value: 1.0
  - sources:
      - model: mistralai/Mistral-7B-v0.1
        layer_range: [24, 32]
merge_method: passthrough
dtype: bfloat16

The axolotl config for fine tuning is available here.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	66.12
AI2 Reasoning Challenge (25-Shot)	64.25
HellaSwag (10-Shot)	83.81
MMLU (5-Shot)	63.66
TruthfulQA (0-shot)	54.66
Winogrande (5-shot)	77.98
GSM8k (5-shot)	52.39