How to use from
SGLangUse 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 "picAIso/code-stral-mini" \
--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": "picAIso/code-stral-mini",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
merge
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the passthrough merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
slices:
- sources:
- model: mistral-community/Codestral-22B-v0.1
layer_range:
- 0
- 8
- sources:
- model: mistral-community/Codestral-22B-v0.1
layer_range:
- 7
- 8
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
- sources:
- model: mistral-community/Codestral-22B-v0.1
layer_range:
- 16
- 24
- sources:
- model: mistral-community/Codestral-22B-v0.1
layer_range:
- 23
- 24
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
- sources:
- model: mistral-community/Codestral-22B-v0.1
layer_range:
- 32
- 40
- sources:
- model: mistral-community/Codestral-22B-v0.1
layer_range:
- 39
- 40
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
- sources:
- model: mistral-community/Codestral-22B-v0.1
layer_range:
- 48
- 56
- sources:
- model: mistral-community/Codestral-22B-v0.1
layer_range:
- 55
- 56
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
merge_method: passthrough
dtype: bfloat16
- Downloads last month
- 6
Model tree for picAIso/code-stral-mini
Base model
mistral-community/Codestral-22B-v0.1
Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "picAIso/code-stral-mini" \ --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": "picAIso/code-stral-mini", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'