Instructions to use lejelly/taskarithmetic-mistral-7b-instrcut-math-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lejelly/taskarithmetic-mistral-7b-instrcut-math-code with Transformers:

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

pipe = pipeline("text-generation", model="lejelly/taskarithmetic-mistral-7b-instrcut-math-code")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lejelly/taskarithmetic-mistral-7b-instrcut-math-code")
model = AutoModelForCausalLM.from_pretrained("lejelly/taskarithmetic-mistral-7b-instrcut-math-code")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use lejelly/taskarithmetic-mistral-7b-instrcut-math-code with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lejelly/taskarithmetic-mistral-7b-instrcut-math-code"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lejelly/taskarithmetic-mistral-7b-instrcut-math-code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/lejelly/taskarithmetic-mistral-7b-instrcut-math-code

SGLang

How to use lejelly/taskarithmetic-mistral-7b-instrcut-math-code 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 "lejelly/taskarithmetic-mistral-7b-instrcut-math-code" \
    --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": "lejelly/taskarithmetic-mistral-7b-instrcut-math-code",
		"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 "lejelly/taskarithmetic-mistral-7b-instrcut-math-code" \
        --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": "lejelly/taskarithmetic-mistral-7b-instrcut-math-code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use lejelly/taskarithmetic-mistral-7b-instrcut-math-code with Docker Model Runner:
```
docker model run hf.co/lejelly/taskarithmetic-mistral-7b-instrcut-math-code
```

taskarithmetic-mistral-7b-instrcut-math-code

File size: 613 Bytes

9c74587

{
  "architectures": [
    "MistralForCausalLM"
  ],
  "attention_dropout": 0.0,
  "bos_token_id": 1,
  "eos_token_id": 2,
  "head_dim": null,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 14336,
  "max_position_embeddings": 32768,
  "model_type": "mistral",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "num_key_value_heads": 8,
  "rms_norm_eps": 1e-05,
  "rope_theta": 10000.0,
  "sliding_window": 4096,
  "tie_word_embeddings": false,
  "torch_dtype": "float16",
  "transformers_version": "4.52.4",
  "use_cache": true,
  "vocab_size": 32000
}