Instructions to use Weyaxi/Einstein-v6.1-Llama3-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Weyaxi/Einstein-v6.1-Llama3-8B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Weyaxi/Einstein-v6.1-Llama3-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Weyaxi/Einstein-v6.1-Llama3-8B")
model = AutoModelForCausalLM.from_pretrained("Weyaxi/Einstein-v6.1-Llama3-8B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Weyaxi/Einstein-v6.1-Llama3-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Weyaxi/Einstein-v6.1-Llama3-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Weyaxi/Einstein-v6.1-Llama3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Weyaxi/Einstein-v6.1-Llama3-8B

SGLang

How to use Weyaxi/Einstein-v6.1-Llama3-8B 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 "Weyaxi/Einstein-v6.1-Llama3-8B" \
    --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": "Weyaxi/Einstein-v6.1-Llama3-8B",
		"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 "Weyaxi/Einstein-v6.1-Llama3-8B" \
        --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": "Weyaxi/Einstein-v6.1-Llama3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Weyaxi/Einstein-v6.1-Llama3-8B with Docker Model Runner:
```
docker model run hf.co/Weyaxi/Einstein-v6.1-Llama3-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🔬 Einstein-v6.1-Llama3-8B

This model is a full fine-tuned version of meta-llama/Meta-Llama-3-8B on diverse datasets.

This model is finetuned using 8xRTX3090 + 1xRTXA6000 using axolotl.

This model's training was sponsored by sablo.ai.

See axolotl config

axolotl version: 0.4.0

base_model: meta-llama/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

chat_template: chatml
datasets:
  - path: data/merged_all.json
    ds_type: json
    type: alpaca
    conversation: chatml

  - path: data/gpteacher-instruct-special-alpaca.json
    ds_type: json
    type: gpteacher
    conversation: chatml

  - path: data/wizardlm_evol_instruct_70k_random_half.json
    ds_type: json
    type: alpaca
    conversation: chatml

  - path: data/capybara_sharegpt.json
    ds_type: json
    type: sharegpt
    conversation: chatml

  - path: data/synthia-v1.3_sharegpt_12500.json
    ds_type: json
    type: sharegpt
    conversation: chatml  

  - path: data/cot_alpaca_gpt4_extracted_openhermes_2.5_sharegpt.json
    ds_type: json
    type: sharegpt
    conversation: chatml

  - path: data/slimorca_dedup_filtered_95k_sharegpt.json
    ds_type: json
    type: sharegpt
    conversation: chatml  

  - path: data/airoboros_3.2_without_contextual_slimorca_orca_sharegpt.json
    ds_type: json
    type: sharegpt
    conversation: chatml  

  - path: data/allenai_wild_chat_gpt4_english_toxic_random_half_4k_sharegpt.json
    ds_type: json
    type: sharegpt
    strict: false
    conversation: chatml  

  - path: data/pippa_bagel_repo_3k_sharegpt.json
    ds_type: json
    type: sharegpt
    conversation: chatml  

  - path: data/gpt4_data_lmys_1m_sharegpt.json
    ds_type: json
    type: sharegpt
    conversation: chatml  

  - path: data/sharegpt_gpt4_english.json
    ds_type: json
    type: sharegpt
    conversation: chatml

  - path: data/no_robots_sharegpt.json
    ds_type: json
    type: sharegpt
    strict: false
    conversation: chatml

  - path: data/oasst_top1_from_fusechatmixture_sharegpt.json
    ds_type: json
    type: sharegpt
    strict: false
    conversation: chatml

  - path: data/everythinglm-data-v3_sharegpt.json
    ds_type: json
    type: sharegpt
    strict: false
    conversation: chatml

dataset_prepared_path: last_run_prepared
val_set_size: 0.002

output_dir: ./Einstein-v6.1-Llama3-8B-model

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false

wandb_project: Einstein
wandb_entity:
wandb_watch:
wandb_name: Einstein-v6.1-Llama3-2-epoch
wandb_log_model:
hub_model_id: Weyaxi/Einstein-v6.1-Llama3-8B

save_safetensors: true

gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_bnb_8bit # look
lr_scheduler: cosine
learning_rate: 0.000005 # look

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 2
eval_table_size:
eval_table_max_new_tokens: 128
saves_per_epoch: 2
debug:

deepspeed: zero3_bf16_cpuoffload_params.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "<|im_end|>"
  unk_token: "<unk>"
  pad_token: <|end_of_text|> # changed
tokens:
  - "<|im_start|>"

💬 Prompt Template

You can use ChatML prompt template while using the model:

ChatML

<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{user}<|im_end|>
<|im_start|>assistant
{asistant}<|im_end|>

This prompt template is available as a chat template, which means you can format messages using the tokenizer.apply_chat_template() method:

messages = [
    {"role": "system", "content": "You are helpful AI asistant."},
    {"role": "user", "content": "Hello!"}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)

📊 Datasets used in this model

The datasets used to train this model are listed in the metadata section of the model card.

Please note that certain datasets mentioned in the metadata may have undergone filtering based on various criteria.

The results of this filtering process and its outcomes are in the data folder of this repository:

Weyaxi/Einstein-v6.1-Llama3-8B/data

🔄 Quantizationed versions

GGUF @bartowski

https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-GGUF

ExLlamaV2 @bartowski

https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-exl2

AWQ @solidrust

https://huggingface.co/solidrust/Einstein-v6.1-Llama3-8B-AWQ

🎯 Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	68.60
AI2 Reasoning Challenge (25-Shot)	62.46
HellaSwag (10-Shot)	82.41
MMLU (5-Shot)	66.19
TruthfulQA (0-shot)	55.10
Winogrande (5-shot)	79.32
GSM8k (5-shot)	66.11

🎯 Open LLM Leaderboard v2 Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	19.99
IFEval (0-Shot)	45.68
BBH (3-Shot)	29.38
MATH Lvl 5 (4-Shot)	5.74
GPQA (0-shot)	4.25
MuSR (0-shot)	11.23
MMLU-PRO (5-shot)	23.68

📚 Some resources, discussions and reviews aboout this model

📱 Octopus-V4-3B

Octopus-V4-3B leverages the incredible physics capabilities of Einstein-v6.1-Llama3-8B in their model.

🤖 Additional information about training

This model is full fine-tuned for 2 epoch.

Total number of steps was 2026.

Loss graph

🤝 Acknowledgments

Thanks to sablo.ai for sponsoring this model.

Thanks to all the dataset authors mentioned in the datasets section.

Thanks to axolotl for making the repository I used to make this model.

Thanks to all open source AI community.

If you would like to support me:

☕ Buy Me a Coffee

Downloads last month: 8,072

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for Weyaxi/Einstein-v6.1-Llama3-8B

Base model

meta-llama/Meta-Llama-3-8B

Finetuned

(607)

this model

Finetunes

1 model

Merges

13 models

Quantizations

11 models

Datasets used to train Weyaxi/Einstein-v6.1-Llama3-8B

Collection including Weyaxi/Einstein-v6.1-Llama3-8B

Einstein Models

Collection

Einstein series • 11 items • Updated Dec 3, 2025 • 9

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

62.460
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

82.410
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

66.190
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

55.100
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

79.320
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

66.110
strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

45.680
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

29.380