Text Generation
Transformers
Safetensors
English
mistral
text-generation-inference
unsloth
conversational
4-bit precision
bitsandbytes
Instructions to use ChevalierJoseph/19OCT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChevalierJoseph/19OCT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ChevalierJoseph/19OCT") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ChevalierJoseph/19OCT") model = AutoModelForCausalLM.from_pretrained("ChevalierJoseph/19OCT", 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]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ChevalierJoseph/19OCT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ChevalierJoseph/19OCT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ChevalierJoseph/19OCT", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ChevalierJoseph/19OCT
- SGLang
How to use ChevalierJoseph/19OCT 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 "ChevalierJoseph/19OCT" \ --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": "ChevalierJoseph/19OCT", "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 "ChevalierJoseph/19OCT" \ --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": "ChevalierJoseph/19OCT", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio
How to use ChevalierJoseph/19OCT with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ChevalierJoseph/19OCT to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ChevalierJoseph/19OCT to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ChevalierJoseph/19OCT to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="ChevalierJoseph/19OCT", max_seq_length=2048, ) - Docker Model Runner
How to use ChevalierJoseph/19OCT with Docker Model Runner:
docker model run hf.co/ChevalierJoseph/19OCT
(Trained with Unsloth)
Browse files- chat_template.jinja +1 -0
- config.json +43 -0
- generation_config.json +8 -0
- model-00001-of-00003.safetensors +3 -0
- model-00002-of-00003.safetensors +3 -0
- model-00003-of-00003.safetensors +3 -0
- model.safetensors.index.json +0 -0
- special_tokens_map.json +30 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +0 -0
chat_template.jinja
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{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% if messages[1]['role'] == 'user' %}{{ '[INST] ' + messages[0]['content'] + ' ' + messages[1]['content'] + ' [/INST]' }}{% set loop_messages = messages[2:] %}{% else %}{{ '[INST] ' + messages[0]['content'] + ' [/INST]' }}{% set loop_messages = messages[1:] %}{% endif %}{% else %}{% set loop_messages = messages %}{% endif %}{% for message in loop_messages %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}
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config.json
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{
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"architectures": [
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"MistralForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 6144,
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"initializer_range": 0.02,
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"intermediate_size": 16384,
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"max_position_embeddings": 131072,
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"model_type": "mistral",
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"num_attention_heads": 48,
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"num_hidden_layers": 56,
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"num_key_value_heads": 8,
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"pad_token_id": 750,
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"quantization_config": {
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"bnb_4bit_compute_dtype": "bfloat16",
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"bnb_4bit_quant_type": "nf4",
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"bnb_4bit_use_double_quant": true,
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"llm_int8_enable_fp32_cpu_offload": false,
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"llm_int8_has_fp16_weight": false,
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"llm_int8_skip_modules": [
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"lm_head"
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],
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"llm_int8_threshold": 6.0,
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"load_in_4bit": true,
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"load_in_8bit": false,
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"quant_method": "bitsandbytes"
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},
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 1000000.0,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.55.4",
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"unsloth_version": "2025.10.7",
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"use_cache": true,
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"vocab_size": 32768
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"max_length": 131072,
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"pad_token_id": 750,
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"transformers_version": "4.55.4"
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}
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model-00001-of-00003.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:ccb4dc49aecc8b123b1c44599e997d3c1362c11cd0dcc1e0a789a942d88db54b
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size 4979766308
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model-00002-of-00003.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:fe802c5adc48c3bde10d1fe9f23e627a614322c18f5676d06e687fecff5b0e7e
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size 4979665834
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model-00003-of-00003.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:0e3b444013d99db614a45797aebdb372bc5532e3f9b593bb7818ac8311e9092a
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size 2116663390
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model.safetensors.index.json
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "[control_748]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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tokenizer.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:59f95e28944c062244741268596badc900df86c7f5ded05088d2da22a7379e06
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size 587583
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tokenizer_config.json
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