Text Generation
Transformers
PyTorch
Safetensors
qwen2
conversational
text-generation-inference
5-bit
exl2
Instructions to use DBMe/Tess-v2.5.2-Qwen2-72B-5.0bpw-h6-exl2-pippa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DBMe/Tess-v2.5.2-Qwen2-72B-5.0bpw-h6-exl2-pippa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DBMe/Tess-v2.5.2-Qwen2-72B-5.0bpw-h6-exl2-pippa") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DBMe/Tess-v2.5.2-Qwen2-72B-5.0bpw-h6-exl2-pippa") model = AutoModelForCausalLM.from_pretrained("DBMe/Tess-v2.5.2-Qwen2-72B-5.0bpw-h6-exl2-pippa", 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 DBMe/Tess-v2.5.2-Qwen2-72B-5.0bpw-h6-exl2-pippa with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DBMe/Tess-v2.5.2-Qwen2-72B-5.0bpw-h6-exl2-pippa" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DBMe/Tess-v2.5.2-Qwen2-72B-5.0bpw-h6-exl2-pippa", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/DBMe/Tess-v2.5.2-Qwen2-72B-5.0bpw-h6-exl2-pippa
- SGLang
How to use DBMe/Tess-v2.5.2-Qwen2-72B-5.0bpw-h6-exl2-pippa 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 "DBMe/Tess-v2.5.2-Qwen2-72B-5.0bpw-h6-exl2-pippa" \ --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": "DBMe/Tess-v2.5.2-Qwen2-72B-5.0bpw-h6-exl2-pippa", "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 "DBMe/Tess-v2.5.2-Qwen2-72B-5.0bpw-h6-exl2-pippa" \ --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": "DBMe/Tess-v2.5.2-Qwen2-72B-5.0bpw-h6-exl2-pippa", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use DBMe/Tess-v2.5.2-Qwen2-72B-5.0bpw-h6-exl2-pippa with Docker Model Runner:
docker model run hf.co/DBMe/Tess-v2.5.2-Qwen2-72B-5.0bpw-h6-exl2-pippa
| # Sample YAML file for configuration. | |
| # Comment and uncomment values as needed. Every value has a default within the application. | |
| # This file serves to be a drop in for config.yml | |
| # Unless specified in the comments, DO NOT put these options in quotes! | |
| # You can use https://www.yamllint.com/ if you want to check your YAML formatting. | |
| # Options for networking | |
| network: | |
| # The IP to host on (default: 127.0.0.1). | |
| # Use 0.0.0.0 to expose on all network adapters | |
| host: 0.0.0.0 | |
| # The port to host on (default: 5000) | |
| port: 5000 | |
| # Disable HTTP token authenticaion with requests | |
| # WARNING: This will make your instance vulnerable! | |
| # Turn on this option if you are ONLY connecting from localhost | |
| disable_auth: False | |
| # Options for logging | |
| logging: | |
| # Enable prompt logging (default: False) | |
| prompt: False | |
| # Enable generation parameter logging (default: False) | |
| generation_params: False | |
| # Options for sampling | |
| sampling: | |
| # Override preset name. Find this in the sampler-overrides folder (default: None) | |
| # This overrides default fallbacks for sampler values that are passed to the API | |
| # Server-side overrides are NOT needed by default | |
| # WARNING: Using this can result in a generation speed penalty | |
| #override_preset: | |
| # Options for development and experimentation | |
| developer: | |
| # Skips exllamav2 version check (default: False) | |
| # It's highly recommended to update your dependencies rather than enabling this flag | |
| # WARNING: Don't set this unless you know what you're doing! | |
| #unsafe_launch: False | |
| # Disable all request streaming (default: False) | |
| # A kill switch for turning off SSE in the API server | |
| #disable_request_streaming: False | |
| # Enable the torch CUDA malloc backend (default: False) | |
| # This can save a few MBs of VRAM, but has a risk of errors. Use at your own risk. | |
| cuda_malloc_backend: True | |
| # Options for model overrides and loading | |
| model: | |
| # Overrides the directory to look for models (default: models) | |
| # Windows users, DO NOT put this path in quotes! This directory will be invalid otherwise. | |
| model_dir: models | |
| # An initial model to load. Make sure the model is located in the model directory! | |
| # A model can be loaded later via the API. | |
| # REQUIRED: This must be filled out to load a model on startup! | |
| model_name: Tess-v2.5.2-Qwen2-72B-safetensors_exl2_5.0bpw | |
| # Sends dummy model names when the models endpoint is queried | |
| # Enable this if the program is looking for a specific OAI model | |
| #use_dummy_models: False | |
| # The below parameters apply only if model_name is set | |
| # Max sequence length (default: Empty) | |
| # Fetched from the model's base sequence length in config.json by default | |
| max_seq_len: 19968 | |
| # Overrides base model context length (default: Empty) | |
| # WARNING: Don't set this unless you know what you're doing! | |
| # Again, do NOT use this for configuring context length, use max_seq_len above ^ | |
| # Only use this if the model's base sequence length in config.json is incorrect (ex. Mistral 7B) | |
| #override_base_seq_len: | |
| # Automatically allocate resources to GPUs (default: True) | |
| # NOTE: Not parsed for single GPU users | |
| gpu_split_auto: True | |
| # Reserve VRAM used for autosplit loading (default: 96 MB on GPU 0) | |
| # This is represented as an array of MB per GPU used | |
| autosplit_reserve: [6] | |
| # An integer array of GBs of vram to split between GPUs (default: []) | |
| # NOTE: Not parsed for single GPU users | |
| #gpu_split: [20.6, 24] | |
| # Rope scale (default: 1.0) | |
| # Same thing as compress_pos_emb | |
| # Only use if your model was trained on long context with rope (check config.json) | |
| # Leave blank to pull the value from the model | |
| #rope_scale: 1.0 | |
| # Rope alpha (default: 1.0) | |
| # Same thing as alpha_value | |
| # Leave blank to automatically calculate alpha | |
| #rope_alpha: 1.0 | |
| # Disable Flash-attention 2. Set to True for GPUs lower than Nvidia's 3000 series. (default: False) | |
| #no_flash_attention: False | |
| # Enable different cache modes for VRAM savings (slight performance hit). | |
| # Possible values FP16, FP8, Q4. (default: FP16) | |
| cache_mode: Q4 | |
| # Chunk size for prompt ingestion. A lower value reduces VRAM usage at the cost of ingestion speed (default: 2048) | |
| # NOTE: Effects vary depending on the model. An ideal value is between 512 and 4096 | |
| chunk_size: 2048 | |
| # Set the prompt template for this model. If empty, attempts to look for the model's chat template. (default: None) | |
| # If a model contains multiple templates in its tokenizer_config.json, set prompt_template to the name | |
| # of the template you want to use.s | |
| # NOTE: Only works with chat completion message lists! | |
| #prompt_template: | |
| # Number of experts to use PER TOKEN. Fetched from the model's config.json if not specified (default: Empty) | |
| # WARNING: Don't set this unless you know what you're doing! | |
| # NOTE: For MoE models (ex. Mixtral) only! | |
| #num_experts_per_token: | |
| # Enables CFG support (default: False) | |
| # WARNING: This flag disables Flash Attention! (a stopgap fix until it's fixed in upstream) | |
| #use_cfg: False | |
| # Enables fasttensors to possibly increase model loading speeds (default: False) | |
| #fasttensors: true | |
| # Options for draft models (speculative decoding). This will use more VRAM! | |
| #draft: | |
| # Overrides the directory to look for draft (default: models) | |
| #draft_model_dir: models | |
| # An initial draft model to load. Make sure this model is located in the model directory! | |
| # A draft model can be loaded later via the API. | |
| #draft_model_name: A model name | |
| # Rope scale for draft models (default: 1.0) | |
| # Same thing as compress_pos_emb | |
| # Only use if your draft model was trained on long context with rope (check config.json) | |
| #draft_rope_scale: 1.0 | |
| # Rope alpha for draft model (default: 1.0) | |
| # Same thing as alpha_value | |
| # Leave blank to automatically calculate alpha value | |
| #draft_rope_alpha: 1.0 | |
| # Options for loras | |
| #lora: | |
| # Overrides the directory to look for loras (default: loras) | |
| #lora_dir: loras | |
| # List of loras to load and associated scaling factors (default: 1.0). Comment out unused entries or add more rows as needed. | |
| #loras: | |
| #- name: lora1 | |
| # scaling: 1.0 | |