Instructions to use QuixiAI/samantha-1.2-mistral-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuixiAI/samantha-1.2-mistral-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="QuixiAI/samantha-1.2-mistral-7b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("QuixiAI/samantha-1.2-mistral-7b") model = AutoModelForCausalLM.from_pretrained("QuixiAI/samantha-1.2-mistral-7b", 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=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use QuixiAI/samantha-1.2-mistral-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QuixiAI/samantha-1.2-mistral-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuixiAI/samantha-1.2-mistral-7b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/QuixiAI/samantha-1.2-mistral-7b
- SGLang
How to use QuixiAI/samantha-1.2-mistral-7b 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 "QuixiAI/samantha-1.2-mistral-7b" \ --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": "QuixiAI/samantha-1.2-mistral-7b", "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 "QuixiAI/samantha-1.2-mistral-7b" \ --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": "QuixiAI/samantha-1.2-mistral-7b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use QuixiAI/samantha-1.2-mistral-7b with Docker Model Runner:
docker model run hf.co/QuixiAI/samantha-1.2-mistral-7b
| base_model: mistralai/Mistral-7B-v0.1 | |
| base_model_config: mistralai/Mistral-7B-v0.1 | |
| model_type: MistralForCausalLM | |
| tokenizer_type: LlamaTokenizer | |
| is_mistral_derived_model: true | |
| load_in_8bit: false | |
| load_in_4bit: false | |
| strict: false | |
| datasets: | |
| - path: ehartford/samantha-data | |
| data_files: samantha-1.1.json | |
| type: sharegpt | |
| conversation: chatml | |
| dataset_prepared_path: last_run_prepared | |
| val_set_size: 0.01 | |
| output_dir: /workspace/samantha-1.2-mistral-7b | |
| sequence_len: 8192 | |
| sample_packing: true | |
| pad_to_sequence_len: true | |
| wandb_project: samantha | |
| wandb_entity: | |
| wandb_watch: | |
| wandb_run_id: | |
| wandb_log_model: | |
| gradient_accumulation_steps: 4 | |
| micro_batch_size: 6 | |
| num_epochs: 40 | |
| adam_beta2: 0.95 | |
| adam_epsilon: 0.00001 | |
| max_grad_norm: 1.0 | |
| lr_scheduler: cosine | |
| learning_rate: 0.000006 | |
| train_on_inputs: false | |
| group_by_length: false | |
| bf16: true | |
| fp16: false | |
| tf32: false | |
| gradient_checkpointing: true | |
| early_stopping_patience: | |
| resume_from_checkpoint: | |
| local_rank: | |
| logging_steps: 1 | |
| xformers_attention: | |
| flash_attention: true | |
| warmup_steps: 100 | |
| eval_steps: 0.05 | |
| eval_table_size: | |
| eval_table_max_new_tokens: | |
| save_steps: | |
| debug: | |
| deepspeed: deepspeed/zero2.json | |
| weight_decay: 0.1 | |
| fsdp: | |
| fsdp_config: | |
| special_tokens: | |
| bos_token: "<s>" | |
| eos_token: "<|im_end|>" | |
| unk_token: "<unk>" | |
| tokens: | |
| - "<|im_start|>" | |
| - "<|im_end|>" | |