Text Generation
Transformers
Safetensors
qwen2
mergekit
Merge
conversational
Eval Results (legacy)
text-generation-inference
Instructions to use bunnycore/Blabbertron-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bunnycore/Blabbertron-1.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bunnycore/Blabbertron-1.0") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bunnycore/Blabbertron-1.0") model = AutoModelForCausalLM.from_pretrained("bunnycore/Blabbertron-1.0", 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use bunnycore/Blabbertron-1.0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bunnycore/Blabbertron-1.0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bunnycore/Blabbertron-1.0", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/bunnycore/Blabbertron-1.0
- SGLang
How to use bunnycore/Blabbertron-1.0 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 "bunnycore/Blabbertron-1.0" \ --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": "bunnycore/Blabbertron-1.0", "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 "bunnycore/Blabbertron-1.0" \ --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": "bunnycore/Blabbertron-1.0", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use bunnycore/Blabbertron-1.0 with Docker Model Runner:
docker model run hf.co/bunnycore/Blabbertron-1.0
|
Download README.md from bunnycore/Blabbertron-1.0: direct link, hf CLI and curl.
- Browser
- Download file 5.77 kB
-
https://huggingface.co/bunnycore/Blabbertron-1.0/resolve/main/README.md
- Command line
-
hf download hf://bunnycore/Blabbertron-1.0/README.md
-
curl -L -o README.md https://huggingface.co/bunnycore/Blabbertron-1.0/resolve/main/README.md
5.77 kB
| library_name: transformers | |
| tags: | |
| - mergekit | |
| - merge | |
| base_model: | |
| - gz987/qwen2.5-7b-cabs-v0.3 | |
| - ngxson/LoRA-Qwen2.5-7B-Instruct-abliterated-v3 | |
| - bunnycore/Qwen2.5-7B-Instruct-Merge-Stock-v0.1 | |
| - Xiaojian9992024/Qwen2.5-Dyanka-7B-Preview | |
| - Qwen/Qwen2.5-7B-Instruct | |
| - bunnycore/Qwen-2.5-7b-s1k-lora_model | |
| - gz987/qwen2.5-7b-cabs-v0.3 | |
| - bunnycore/Qwen-2.5-7b-rp-lora | |
| - Qwen/Qwen2.5-7B-Instruct | |
| - ngxson/LoRA-Qwen2.5-7B-Instruct-abliterated-v3 | |
| language: | |
| - zho | |
| - eng | |
| - fra | |
| - spa | |
| - por | |
| - deu | |
| - ita | |
| - rus | |
| - jpn | |
| - kor | |
| - vie | |
| - tha | |
| - ara | |
| model-index: | |
| - name: Blabbertron-1.0 | |
| results: | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: IFEval (0-Shot) | |
| type: HuggingFaceH4/ifeval | |
| args: | |
| num_few_shot: 0 | |
| metrics: | |
| - type: inst_level_strict_acc and prompt_level_strict_acc | |
| value: 74.33 | |
| name: strict accuracy | |
| source: | |
| url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Blabbertron-1.0 | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: BBH (3-Shot) | |
| type: BBH | |
| args: | |
| num_few_shot: 3 | |
| metrics: | |
| - type: acc_norm | |
| value: 36.05 | |
| name: normalized accuracy | |
| source: | |
| url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Blabbertron-1.0 | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: MATH Lvl 5 (4-Shot) | |
| type: hendrycks/competition_math | |
| args: | |
| num_few_shot: 4 | |
| metrics: | |
| - type: exact_match | |
| value: 49.24 | |
| name: exact match | |
| source: | |
| url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Blabbertron-1.0 | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: GPQA (0-shot) | |
| type: Idavidrein/gpqa | |
| args: | |
| num_few_shot: 0 | |
| metrics: | |
| - type: acc_norm | |
| value: 6.94 | |
| name: acc_norm | |
| source: | |
| url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Blabbertron-1.0 | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: MuSR (0-shot) | |
| type: TAUR-Lab/MuSR | |
| args: | |
| num_few_shot: 0 | |
| metrics: | |
| - type: acc_norm | |
| value: 13.51 | |
| name: acc_norm | |
| source: | |
| url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Blabbertron-1.0 | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: MMLU-PRO (5-shot) | |
| type: TIGER-Lab/MMLU-Pro | |
| config: main | |
| split: test | |
| args: | |
| num_few_shot: 5 | |
| metrics: | |
| - type: acc | |
| value: 37.27 | |
| name: accuracy | |
| source: | |
| url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Blabbertron-1.0 | |
| name: Open LLM Leaderboard | |
| # merge | |
| This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). | |
| ## Merge Details | |
| ### Merge Method | |
| This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) + [ngxson/LoRA-Qwen2.5-7B-Instruct-abliterated-v3](https://huggingface.co/ngxson/LoRA-Qwen2.5-7B-Instruct-abliterated-v3) as a base. | |
| ### Models Merged | |
| The following models were included in the merge: | |
| * [gz987/qwen2.5-7b-cabs-v0.3](https://huggingface.co/gz987/qwen2.5-7b-cabs-v0.3) + [ngxson/LoRA-Qwen2.5-7B-Instruct-abliterated-v3](https://huggingface.co/ngxson/LoRA-Qwen2.5-7B-Instruct-abliterated-v3) | |
| * [bunnycore/Qwen2.5-7B-Instruct-Merge-Stock-v0.1](https://huggingface.co/bunnycore/Qwen2.5-7B-Instruct-Merge-Stock-v0.1) | |
| * [Xiaojian9992024/Qwen2.5-Dyanka-7B-Preview](https://huggingface.co/Xiaojian9992024/Qwen2.5-Dyanka-7B-Preview) | |
| * [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) + [bunnycore/Qwen-2.5-7b-s1k-lora_model](https://huggingface.co/bunnycore/Qwen-2.5-7b-s1k-lora_model) | |
| * [gz987/qwen2.5-7b-cabs-v0.3](https://huggingface.co/gz987/qwen2.5-7b-cabs-v0.3) + [bunnycore/Qwen-2.5-7b-rp-lora](https://huggingface.co/bunnycore/Qwen-2.5-7b-rp-lora) | |
| ### Configuration | |
| The following YAML configuration was used to produce this model: | |
| ```yaml | |
| models: | |
| - model: Qwen/Qwen2.5-7B-Instruct+bunnycore/Qwen-2.5-7b-s1k-lora_model | |
| parameters: | |
| weight: 0.3 | |
| - model: Xiaojian9992024/Qwen2.5-Dyanka-7B-Preview | |
| - model: bunnycore/Qwen2.5-7B-Instruct-Merge-Stock-v0.1 | |
| - model: gz987/qwen2.5-7b-cabs-v0.3+ngxson/LoRA-Qwen2.5-7B-Instruct-abliterated-v3 | |
| - model: gz987/qwen2.5-7b-cabs-v0.3+bunnycore/Qwen-2.5-7b-rp-lora | |
| base_model: Qwen/Qwen2.5-7B-Instruct+ngxson/LoRA-Qwen2.5-7B-Instruct-abliterated-v3 | |
| merge_method: model_stock | |
| parameters: | |
| dtype: bfloat16 | |
| tokenizer_source: Qwen/Qwen2.5-7B-Instruct | |
| ``` | |
| # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) | |
| Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/bunnycore__Blabbertron-1.0-details) | |
| | Metric |Value| | |
| |-------------------|----:| | |
| |Avg. |36.22| | |
| |IFEval (0-Shot) |74.33| | |
| |BBH (3-Shot) |36.05| | |
| |MATH Lvl 5 (4-Shot)|49.24| | |
| |GPQA (0-shot) | 6.94| | |
| |MuSR (0-shot) |13.51| | |
| |MMLU-PRO (5-shot) |37.27| | |