Text Generation
Transformers
Safetensors
llama
llama-factory
full
Generated from Trainer
conversational
text-generation-inference
Instructions to use formalmathatepfl/llama-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use formalmathatepfl/llama-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="formalmathatepfl/llama-finetuned") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("formalmathatepfl/llama-finetuned") model = AutoModelForCausalLM.from_pretrained("formalmathatepfl/llama-finetuned") 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
- vLLM
How to use formalmathatepfl/llama-finetuned with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "formalmathatepfl/llama-finetuned" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "formalmathatepfl/llama-finetuned", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/formalmathatepfl/llama-finetuned
- SGLang
How to use formalmathatepfl/llama-finetuned 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 "formalmathatepfl/llama-finetuned" \ --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": "formalmathatepfl/llama-finetuned", "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 "formalmathatepfl/llama-finetuned" \ --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": "formalmathatepfl/llama-finetuned", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use formalmathatepfl/llama-finetuned with Docker Model Runner:
docker model run hf.co/formalmathatepfl/llama-finetuned
Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- README.md +61 -0
- all_results.json +8 -0
- chat_template.jinja +5 -0
- config.json +32 -0
- generation_config.json +13 -0
- model.safetensors +3 -0
- tokenizer.json +3 -0
- tokenizer_config.json +16 -0
- train_results.json +8 -0
- trainer_log.jsonl +0 -0
- trainer_state.json +0 -0
- training_args.bin +3 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json 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: other
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base_model: meta-llama/Llama-3-8B-Instruct
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tags:
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- llama-factory
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- full
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- generated_from_trainer
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model-index:
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- name: finetuning_llama3_8b
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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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# finetuning_llama3_8b
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This model is a fine-tuned version of meta-llama/Llama-3-8B-Instruct.
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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: 0.0001
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- train_batch_size: 2
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- eval_batch_size: 8
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 4
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 16
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- total_eval_batch_size: 32
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 0.03
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- num_epochs: 1.0
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### Training results
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### Framework versions
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- Transformers 5.2.0
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- Pytorch 2.6.0+cu124
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- Datasets 4.0.0
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- Tokenizers 0.22.2
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all_results.json
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{
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"epoch": 1.0,
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"total_flos": 4428379717632000.0,
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"train_loss": 0.036302091460815894,
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"train_runtime": 70628.121,
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"train_samples_per_second": 2.396,
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"train_steps_per_second": 0.15
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}
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chat_template.jinja
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{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>
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'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>
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' }}{% endif %}
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"dtype": "bfloat16",
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"eos_token_id": 128009,
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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": 8192,
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"mlp_bias": false,
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"model_type": "llama",
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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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"pad_token_id": 128009,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
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"rope_theta": 500000.0,
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"rope_type": "default"
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},
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"tie_word_embeddings": false,
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"transformers_version": "5.2.0",
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"use_cache": false,
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"vocab_size": 128256
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}
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generation_config.json
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{
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"bos_token_id": 128000,
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"do_sample": true,
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"eos_token_id": [
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128001,
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128009
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],
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"max_length": 4096,
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"pad_token_id": 128009,
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "5.2.0"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:77c32eb0d3de2cca9966bee7e3f9fd4987a8f99636bf3f689cdde95bc64b1deb
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size 16060556616
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:3c5cf44023714fb39b05e71e425f8d7b92805ff73f7988b083b8c87f0bf87393
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size 17209961
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"bos_token": "<|begin_of_text|>",
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"clean_up_tokenization_spaces": true,
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"eos_token": "<|eot_id|>",
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"is_local": true,
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"model_input_names": [
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"input_ids",
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"attention_mask"
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],
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<|eot_id|>",
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"padding_side": "right",
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"split_special_tokens": false,
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"tokenizer_class": "TokenizersBackend"
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}
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train_results.json
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{
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"epoch": 1.0,
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"total_flos": 4428379717632000.0,
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"train_loss": 0.036302091460815894,
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"train_runtime": 70628.121,
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"train_samples_per_second": 2.396,
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"train_steps_per_second": 0.15
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}
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trainer_log.jsonl
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trainer_state.json
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:07bfd3debf0ad31494f0561180f7c0440331eb6e795c1cf40128eafb080bc03c
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size 7096
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