153 lines
3.1 KiB
Markdown
153 lines
3.1 KiB
Markdown
---
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base_model: macadeliccc/Samantha-Qwen-2-7B
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datasets:
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- macadeliccc/opus_samantha
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- HuggingfaceH4/ultrachat_200k
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- teknium/OpenHermes-2.5
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- Sao10K/Claude-3-Opus-Instruct-15K
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license: apache-2.0
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language:
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- en
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- zh
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pipeline_tag: text-generation
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---
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# Samantha Qwen2 7B-GGUF
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This is quantized version of [macadeliccc/Samantha-Qwen-2-7B](https://huggingface.co/macadeliccc/Samantha-Qwen-2-7B) created using llama.cpp
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# Model Description
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Trained on 2x4090 using QLoRa and FSDP
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+ [LoRa](macadeliccc/Samantha-Qwen2-7B-LoRa)
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## Launch Using VLLM
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```bash
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python -m vllm.entrypoints.openai.api_server \
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--model macadeliccc/Samantha-Qwen-2-7B \
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--chat-template ./examples/template_chatml.jinja \
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```
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```python
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from openai import OpenAI
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# Set OpenAI's API key and API base to use vLLM's API server.
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openai_api_key = "EMPTY"
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openai_api_base = "http://localhost:8000/v1"
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client = OpenAI(
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api_key=openai_api_key,
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base_url=openai_api_base,
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)
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chat_response = client.chat.completions.create(
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model="macadeliccc/Samantha-Qwen-2-7B",
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messages=[
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Tell me a joke."},
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]
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)
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print("Chat response:", chat_response)
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```
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## Prompt Template
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```
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<|im_start|>system
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You are a friendly assistant.<|im_end|>
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<|im_start|>user
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What is the capital of France?<|im_end|>
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<|im_start|>assistant
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The capital of France is Paris.
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```
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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<details><summary>See axolotl config</summary>
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axolotl version: `0.4.0`
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```yaml
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base_model: Qwen/Qwen-7B
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model_type: AutoModelForCausalLM
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tokenizer_type: AutoTokenizer
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trust_remote_code: true
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load_in_8bit: false
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load_in_4bit: true
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strict: false
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datasets:
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- path: macadeliccc/opus_samantha
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type: sharegpt
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field: conversations
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conversation: chatml
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- path: uncensored-ultrachat.json
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type: sharegpt
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field: conversations
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conversation: chatml
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- path: openhermes_200k.json
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type: sharegpt
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field: conversations
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conversation: chatml
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- path: opus_instruct.json
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type: sharegpt
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field: conversations
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conversation: chatml
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chat_template: chatml
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dataset_prepared_path:
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val_set_size: 0.05
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output_dir: ./outputs/lora-out
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sequence_len: 2048
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sample_packing: false
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pad_to_sequence_len:
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adapter: qlora
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lora_model_dir:
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lora_r: 32
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lora_alpha: 16
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lora_dropout: 0.05
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lora_target_linear: true
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lora_fan_in_fan_out:
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wandb_project:
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wandb_entity:
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wandb_watch:
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wandb_name:
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wandb_log_model:
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gradient_accumulation_steps: 4
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micro_batch_size: 2
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num_epochs: 1
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optimizer: adamw_bnb_8bit
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lr_scheduler: cosine
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learning_rate: 0.0002
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train_on_inputs: false
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group_by_length: false
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bf16: auto
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fp16:
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tf32: false
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gradient_checkpointing: false
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early_stopping_patience:
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resume_from_checkpoint:
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local_rank:
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logging_steps: 1
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xformers_attention:
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flash_attention:
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warmup_steps: 250
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evals_per_epoch: 4
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eval_table_size:
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eval_max_new_tokens: 128
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saves_per_epoch: 1
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debug:
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deepspeed:
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weight_decay: 0.0
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fsdp:
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fsdp_config:
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special_tokens:
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```
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</details><br> |