Instructions to use EleutherAI/llemma_34b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EleutherAI/llemma_34b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="EleutherAI/llemma_34b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("EleutherAI/llemma_34b") model = AutoModelForCausalLM.from_pretrained("EleutherAI/llemma_34b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use EleutherAI/llemma_34b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "EleutherAI/llemma_34b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "EleutherAI/llemma_34b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/EleutherAI/llemma_34b
- SGLang
How to use EleutherAI/llemma_34b 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 "EleutherAI/llemma_34b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "EleutherAI/llemma_34b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "EleutherAI/llemma_34b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "EleutherAI/llemma_34b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use EleutherAI/llemma_34b with Docker Model Runner:
docker model run hf.co/EleutherAI/llemma_34b
Adding Evaluation Results
#4
by leaderboard-pr-bot - opened
README.md
CHANGED
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---
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license: llama2
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datasets:
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- EleutherAI/proof-pile-2
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- open-web-math/open-web-math
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language:
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- en
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tags:
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- math
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- reasoning
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---
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<img src="llemma.png" width="400">
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@@ -66,4 +169,17 @@ In addition to chain-of-thought reasoning, Llemma has strong capabilities in com
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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-
```
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| 1 |
---
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language:
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- en
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+
license: llama2
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tags:
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| 6 |
- math
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- reasoning
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| 8 |
+
datasets:
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+
- EleutherAI/proof-pile-2
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+
- open-web-math/open-web-math
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+
model-index:
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+
- name: llemma_34b
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results:
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+
- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: AI2 Reasoning Challenge (25-Shot)
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type: ai2_arc
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config: ARC-Challenge
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split: test
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args:
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num_few_shot: 25
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+
metrics:
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+
- type: acc_norm
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+
value: 55.29
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+
name: normalized accuracy
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+
source:
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| 29 |
+
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=EleutherAI/llemma_34b
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name: Open LLM Leaderboard
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+
- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: HellaSwag (10-Shot)
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type: hellaswag
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: acc_norm
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value: 75.08
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name: normalized accuracy
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+
source:
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| 45 |
+
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=EleutherAI/llemma_34b
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name: Open LLM Leaderboard
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+
- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU (5-Shot)
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type: cais/mmlu
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config: all
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 58.93
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name: accuracy
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+
source:
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| 62 |
+
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=EleutherAI/llemma_34b
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name: Open LLM Leaderboard
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| 64 |
+
- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: TruthfulQA (0-shot)
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type: truthful_qa
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config: multiple_choice
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split: validation
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args:
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num_few_shot: 0
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metrics:
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- type: mc2
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value: 40.31
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+
source:
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| 78 |
+
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=EleutherAI/llemma_34b
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| 79 |
+
name: Open LLM Leaderboard
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| 80 |
+
- task:
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| 81 |
+
type: text-generation
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| 82 |
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name: Text Generation
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dataset:
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name: Winogrande (5-shot)
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type: winogrande
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config: winogrande_xl
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split: validation
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args:
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num_few_shot: 5
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metrics:
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| 91 |
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- type: acc
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value: 75.53
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| 93 |
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name: accuracy
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| 94 |
+
source:
|
| 95 |
+
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=EleutherAI/llemma_34b
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| 96 |
+
name: Open LLM Leaderboard
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| 97 |
+
- task:
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| 98 |
+
type: text-generation
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| 99 |
+
name: Text Generation
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| 100 |
+
dataset:
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name: GSM8k (5-shot)
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| 102 |
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type: gsm8k
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| 103 |
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config: main
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| 104 |
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split: test
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args:
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num_few_shot: 5
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+
metrics:
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+
- type: acc
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| 109 |
+
value: 50.87
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| 110 |
+
name: accuracy
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| 111 |
+
source:
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| 112 |
+
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=EleutherAI/llemma_34b
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| 113 |
+
name: Open LLM Leaderboard
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| 114 |
---
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| 115 |
<img src="llemma.png" width="400">
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| 116 |
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| 169 |
archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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| 172 |
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```
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| 173 |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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| 174 |
+
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__llemma_34b)
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+
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| Metric |Value|
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| 177 |
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|---------------------------------|----:|
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| 178 |
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|Avg. |59.34|
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| 179 |
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|AI2 Reasoning Challenge (25-Shot)|55.29|
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| 180 |
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|HellaSwag (10-Shot) |75.08|
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| 181 |
+
|MMLU (5-Shot) |58.93|
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| 182 |
+
|TruthfulQA (0-shot) |40.31|
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| 183 |
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|Winogrande (5-shot) |75.53|
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| 184 |
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|GSM8k (5-shot) |50.87|
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| 185 |
+
|