Text Generation
Transformers
PyTorch
GGUF
mistral
Generated from Trainer
conversational
text-generation-inference
Instructions to use Heralax/Mistrilitary-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Heralax/Mistrilitary-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Heralax/Mistrilitary-7b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Heralax/Mistrilitary-7b") model = AutoModelForCausalLM.from_pretrained("Heralax/Mistrilitary-7b") 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]:])) - llama-cpp-python
How to use Heralax/Mistrilitary-7b with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Heralax/Mistrilitary-7b", filename="army-pretrain-7.2B-1-F16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Heralax/Mistrilitary-7b with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Heralax/Mistrilitary-7b:F16 # Run inference directly in the terminal: llama-cli -hf Heralax/Mistrilitary-7b:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Heralax/Mistrilitary-7b:F16 # Run inference directly in the terminal: llama-cli -hf Heralax/Mistrilitary-7b:F16
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Heralax/Mistrilitary-7b:F16 # Run inference directly in the terminal: ./llama-cli -hf Heralax/Mistrilitary-7b:F16
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Heralax/Mistrilitary-7b:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Heralax/Mistrilitary-7b:F16
Use Docker
docker model run hf.co/Heralax/Mistrilitary-7b:F16
- LM Studio
- Jan
- vLLM
How to use Heralax/Mistrilitary-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Heralax/Mistrilitary-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": "Heralax/Mistrilitary-7b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Heralax/Mistrilitary-7b:F16
- SGLang
How to use Heralax/Mistrilitary-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 "Heralax/Mistrilitary-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": "Heralax/Mistrilitary-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 "Heralax/Mistrilitary-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": "Heralax/Mistrilitary-7b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use Heralax/Mistrilitary-7b with Ollama:
ollama run hf.co/Heralax/Mistrilitary-7b:F16
- Unsloth Studio
How to use Heralax/Mistrilitary-7b 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 Heralax/Mistrilitary-7b 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 Heralax/Mistrilitary-7b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Heralax/Mistrilitary-7b to start chatting
- Docker Model Runner
How to use Heralax/Mistrilitary-7b with Docker Model Runner:
docker model run hf.co/Heralax/Mistrilitary-7b:F16
- Lemonade
How to use Heralax/Mistrilitary-7b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Heralax/Mistrilitary-7b:F16
Run and chat with the model
lemonade run user.Mistrilitary-7b-F16
List all available models
lemonade list
Commit ·
3a56d37
0
Parent(s):
files
Browse files- .gitattributes +2 -0
- README.md +143 -0
- added_tokens.json +3 -0
- army-pretrain-7.2B-1-F16.gguf +3 -0
- config.json +27 -0
- generation_config.json +7 -0
- ggml-model-Q8_0.gguf +3 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +30 -0
- tokenizer.json +0 -0
- tokenizer.model +0 -0
- tokenizer_config.json +51 -0
.gitattributes
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*.gguf filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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library_name: transformers
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license: apache-2.0
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base_model: Heralax/army-pretrain-1
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tags:
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- generated_from_trainer
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model-index:
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- name: us-army-finetune-1
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
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<details><summary>See axolotl config</summary>
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axolotl version: `0.4.1`
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```yaml
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base_model: Heralax/army-pretrain-1
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tokenizer_type: AutoTokenizer
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is_mistral_derived_model: true
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load_in_8bit: false
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load_in_4bit: false
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strict: false
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datasets:
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- path: json
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data_files: us_army_plain_qa_list_open.jsonl
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ds_type: json
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type: sharegpt
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conversation: chatml
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- path: json
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data_files: us_army_plain_qa_list_vanilla.jsonl
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ds_type: json
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type: sharegpt
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conversation: chatml
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- path: json
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data_files: us_army_plain_qa_list_negative.jsonl
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ds_type: json
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type: sharegpt
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conversation: chatml
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dataset_prepared_path: last_run_prepared
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output_dir: ./us-army-finetune-1
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sequence_len: 4096
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sample_packing: true
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pad_to_sequence_len: true
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shuffle_merged_datasets: true
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wandb_project: mistral-usarmy
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wandb_entity:
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wandb_watch:
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wandb_run_id:
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wandb_log_model:
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gradient_accumulation_steps: 6
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micro_batch_size: 2
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eval_batch_size: 1
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num_epochs: 6
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optimizer: paged_adamw_8bit
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lr_scheduler: cosine
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learning_rate: 0.000020
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weight_decay: 0
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# Gradient clipping max norm
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max_grad_norm: 1.0
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noisy_embedding_alpha: 0
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train_on_inputs: false
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group_by_length: false
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bf16: true
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fp16: false
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tf32: false
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gradient_checkpointing: unsloth
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early_stopping_patience:
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resume_from_checkpoint:
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logging_steps: 1
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xformers_attention:
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flash_attention: true
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chat_template: chatml
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warmup_ratio: 0.5
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auto_resume_from_checkpoints: false
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#warmup_ratio: 0.5
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eval_steps: 10
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saves_per_epoch: 1
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eval_sample_packing: false
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save_total_limit: 3
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debug:
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deepspeed: deepspeed_configs/zero2.json
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special_tokens:
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pad_token: "<|end_of_text|>"
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```
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</details><br>
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# us-army-finetune-1
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This model is a fine-tuned version of [Heralax/army-pretrain-1](https://huggingface.co/Heralax/army-pretrain-1) on the None dataset.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 2
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- eval_batch_size: 1
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 5
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- gradient_accumulation_steps: 6
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- total_train_batch_size: 60
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- total_eval_batch_size: 5
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 48
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- num_epochs: 6
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### Training results
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### Framework versions
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- Transformers 4.45.0
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- Pytorch 2.3.1+cu121
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- Datasets 2.21.0
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- Tokenizers 0.20.0
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added_tokens.json
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{
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"<|end_of_text|>": 32000
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}
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army-pretrain-7.2B-1-F16.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:f7deb6d836e1bcad4d8582b9c7acd14c0ed40fe553aa370f81a12e7efffa4974
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size 14484749216
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config.json
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{
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"_name_or_path": "Heralax/army-pretrain-1",
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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": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 32768,
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"model_type": "mistral",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-05,
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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": "4.45.0",
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"use_cache": false,
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"vocab_size": 32001
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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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"do_sample": true,
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"eos_token_id": 2,
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"transformers_version": "4.45.0"
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}
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ggml-model-Q8_0.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:a55e3780edf63b1a5c8a922b67336f7bbd417c02f3c7ec998677bc080cf88832
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size 7695867296
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:b829aa20aade9424fa731063076ac1d0261433af53b3c9df52aad3d233556d67
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size 14483521198
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special_tokens_map.json
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| 1 |
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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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| 5 |
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"normalized": false,
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| 6 |
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"rstrip": false,
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| 7 |
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"single_word": false
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},
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| 9 |
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"eos_token": {
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| 10 |
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"content": "</s>",
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| 11 |
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"lstrip": false,
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| 12 |
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"normalized": false,
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"rstrip": false,
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| 14 |
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"single_word": false
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| 15 |
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},
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| 16 |
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"pad_token": {
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| 17 |
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"content": "<|end_of_text|>",
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| 18 |
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"lstrip": false,
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| 19 |
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"normalized": false,
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| 20 |
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"rstrip": false,
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| 21 |
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"single_word": false
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| 22 |
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},
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| 23 |
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"unk_token": {
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| 24 |
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"content": "<unk>",
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| 25 |
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"lstrip": false,
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| 26 |
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"normalized": false,
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| 27 |
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"rstrip": false,
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| 28 |
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"single_word": false
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| 29 |
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}
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| 30 |
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}
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tokenizer.json
ADDED
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The diff for this file is too large to render.
See raw diff
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tokenizer.model
ADDED
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Binary file (493 kB). View file
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tokenizer_config.json
ADDED
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@@ -0,0 +1,51 @@
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| 1 |
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{
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| 2 |
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"add_bos_token": true,
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| 3 |
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"add_eos_token": false,
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| 4 |
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"add_prefix_space": true,
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| 5 |
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"added_tokens_decoder": {
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| 6 |
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"0": {
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| 7 |
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"content": "<unk>",
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| 8 |
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"lstrip": false,
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| 9 |
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"normalized": false,
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| 10 |
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"rstrip": false,
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| 11 |
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"single_word": false,
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| 12 |
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"special": true
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| 13 |
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},
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| 14 |
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"1": {
|
| 15 |
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"content": "<s>",
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| 16 |
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"lstrip": false,
|
| 17 |
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"normalized": false,
|
| 18 |
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"rstrip": false,
|
| 19 |
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"single_word": false,
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| 20 |
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"special": true
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| 21 |
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},
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| 22 |
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"2": {
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| 23 |
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"content": "</s>",
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| 24 |
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"lstrip": false,
|
| 25 |
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"normalized": false,
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| 26 |
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"rstrip": false,
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| 27 |
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"single_word": false,
|
| 28 |
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"special": true
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| 29 |
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},
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| 30 |
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"32000": {
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| 31 |
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"content": "<|end_of_text|>",
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| 32 |
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"lstrip": false,
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| 33 |
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"normalized": false,
|
| 34 |
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"rstrip": false,
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| 35 |
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"single_word": false,
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| 36 |
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"special": true
|
| 37 |
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}
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| 38 |
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},
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| 39 |
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"bos_token": "<s>",
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| 40 |
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"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
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| 41 |
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"clean_up_tokenization_spaces": false,
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| 42 |
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"eos_token": "</s>",
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| 43 |
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"legacy": true,
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| 44 |
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"model_max_length": 1000000000000000019884624838656,
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| 45 |
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"pad_token": "<|end_of_text|>",
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| 46 |
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"sp_model_kwargs": {},
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| 47 |
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"spaces_between_special_tokens": false,
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| 48 |
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"tokenizer_class": "LlamaTokenizer",
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| 49 |
+
"unk_token": "<unk>",
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| 50 |
+
"use_default_system_prompt": false
|
| 51 |
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}
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