Text Generation
Transformers
TensorBoard
Safetensors
mistral
alignment-handbook
Generated from Trainer
conversational
text-generation-inference
Instructions to use maxidl/Mistral-7B-v0.1-capybara-orpo-en-de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maxidl/Mistral-7B-v0.1-capybara-orpo-en-de with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="maxidl/Mistral-7B-v0.1-capybara-orpo-en-de") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("maxidl/Mistral-7B-v0.1-capybara-orpo-en-de") model = AutoModelForCausalLM.from_pretrained("maxidl/Mistral-7B-v0.1-capybara-orpo-en-de", 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 maxidl/Mistral-7B-v0.1-capybara-orpo-en-de with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "maxidl/Mistral-7B-v0.1-capybara-orpo-en-de" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "maxidl/Mistral-7B-v0.1-capybara-orpo-en-de", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/maxidl/Mistral-7B-v0.1-capybara-orpo-en-de
- SGLang
How to use maxidl/Mistral-7B-v0.1-capybara-orpo-en-de 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 "maxidl/Mistral-7B-v0.1-capybara-orpo-en-de" \ --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": "maxidl/Mistral-7B-v0.1-capybara-orpo-en-de", "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 "maxidl/Mistral-7B-v0.1-capybara-orpo-en-de" \ --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": "maxidl/Mistral-7B-v0.1-capybara-orpo-en-de", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use maxidl/Mistral-7B-v0.1-capybara-orpo-en-de with Docker Model Runner:
docker model run hf.co/maxidl/Mistral-7B-v0.1-capybara-orpo-en-de
Upload folder using huggingface_hub
Browse files- README.md +59 -0
- all_results.json +8 -0
- config.json +26 -0
- generation_config.json +6 -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 +298 -0
- runs/Apr15_17-36-59_ggpu163/events.out.tfevents.1713195547.ggpu163.2260874.0 +3 -0
- special_tokens_map.json +24 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +43 -0
- train_results.json +8 -0
- trainer_state.json +516 -0
- training_args.bin +3 -0
README.md
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| 1 |
+
---
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+
base_model: mistralai/Mistral-7B-v0.1
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tags:
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- alignment-handbook
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- generated_from_trainer
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datasets:
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- maxidl/distilabel-capybara-dpo-7k-binarized_en_de
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model-index:
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- name: Mistral-7B-v0.1-capybara-orpo-en-de
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results: []
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---
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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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# Mistral-7B-v0.1-capybara-orpo-en-de
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This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the maxidl/distilabel-capybara-dpo-7k-binarized_en_de 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: 5e-06
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- train_batch_size: 4
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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: 32
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- total_train_batch_size: 128
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- total_eval_batch_size: 256
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: inverse_sqrt
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- lr_scheduler_warmup_steps: 100
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- num_epochs: 3
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### Training results
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### Framework versions
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- Transformers 4.39.3
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- Pytorch 2.1.2
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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all_results.json
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{
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"epoch": 3.0,
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"train_loss": 0.649145109749539,
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"train_runtime": 4268.1705,
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| 5 |
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"train_samples": 11530,
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+
"train_samples_per_second": 8.104,
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"train_steps_per_second": 0.064
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}
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config.json
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{
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"_name_or_path": "mistralai/Mistral-7B-v0.1",
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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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"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": 10000.0,
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"sliding_window": 4096,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.39.3",
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"use_cache": true,
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"vocab_size": 32000
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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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| 4 |
+
"eos_token_id": 2,
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"transformers_version": "4.39.3"
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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 sha256:056ed418415554f05069301338398b9b9334b179422c0742528d233f95ce257b
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+
size 4943162336
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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 sha256:37135ccd1e8747eaee06af0b760d16de278d59067f5eb7c88cf272c879c9730f
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+
size 4999819336
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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:cd5588779b0dee3055d50ecd18ac217f059f5e9e0c4d99592645f0d914034f93
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| 3 |
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size 4540516344
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model.safetensors.index.json
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| 1 |
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{
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| 2 |
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"metadata": {
|
| 3 |
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"total_size": 14483464192
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| 4 |
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},
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| 5 |
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"weight_map": {
|
| 6 |
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"lm_head.weight": "model-00003-of-00003.safetensors",
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| 7 |
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"model.embed_tokens.weight": "model-00001-of-00003.safetensors",
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| 8 |
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"model.layers.0.input_layernorm.weight": "model-00001-of-00003.safetensors",
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"model.layers.0.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
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"model.layers.0.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
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"model.layers.0.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
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training_args.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:28b00013854d73dd32b49a09ad19cc8651054680a280196abbf9e2866c48d694
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| 3 |
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size 5368
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