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RichardErkhov/
Weyaxi_-_Einstein-v7-Qwen2-7B-gguf

Name: Weyaxi_-_Einstein-v7-Qwen2-7B-gguf
Availability: InStock

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Einstein-v7-Qwen2-7B - GGUF

Model creator: https://huggingface.co/Weyaxi/
Original model: https://huggingface.co/Weyaxi/Einstein-v7-Qwen2-7B/

Name	Quant method	Size
Einstein-v7-Qwen2-7B.Q2_K.gguf	Q2_K	2.81GB
Einstein-v7-Qwen2-7B.IQ3_XS.gguf	IQ3_XS	3.12GB
Einstein-v7-Qwen2-7B.IQ3_S.gguf	IQ3_S	3.26GB
Einstein-v7-Qwen2-7B.Q3_K_S.gguf	Q3_K_S	3.25GB
Einstein-v7-Qwen2-7B.IQ3_M.gguf	IQ3_M	3.33GB
Einstein-v7-Qwen2-7B.Q3_K.gguf	Q3_K	3.55GB
Einstein-v7-Qwen2-7B.Q3_K_M.gguf	Q3_K_M	3.55GB
Einstein-v7-Qwen2-7B.Q3_K_L.gguf	Q3_K_L	3.81GB
Einstein-v7-Qwen2-7B.IQ4_XS.gguf	IQ4_XS	3.96GB
Einstein-v7-Qwen2-7B.Q4_0.gguf	Q4_0	4.13GB
Einstein-v7-Qwen2-7B.IQ4_NL.gguf	IQ4_NL	4.16GB
Einstein-v7-Qwen2-7B.Q4_K_S.gguf	Q4_K_S	4.15GB
Einstein-v7-Qwen2-7B.Q4_K.gguf	Q4_K	3.42GB
Einstein-v7-Qwen2-7B.Q4_K_M.gguf	Q4_K_M	1.86GB
Einstein-v7-Qwen2-7B.Q4_1.gguf	Q4_1	1.47GB
Einstein-v7-Qwen2-7B.Q5_0.gguf	Q5_0	1.47GB
Einstein-v7-Qwen2-7B.Q5_K_S.gguf	Q5_K_S	4.95GB
Einstein-v7-Qwen2-7B.Q5_K.gguf	Q5_K	4.16GB
Einstein-v7-Qwen2-7B.Q5_K_M.gguf	Q5_K_M	2.21GB
Einstein-v7-Qwen2-7B.Q5_1.gguf	Q5_1	1.69GB
Einstein-v7-Qwen2-7B.Q6_K.gguf	Q6_K	1.68GB
Einstein-v7-Qwen2-7B.Q8_0.gguf	Q8_0	1.67GB

Original model description:

language:

en license: other tags:
axolotl
instruct
finetune
chatml
gpt4
synthetic data
science
physics
chemistry
biology
math
qwen
qwen2 base_model: Qwen/Qwen2-7B datasets:
allenai/ai2_arc
camel-ai/physics
camel-ai/chemistry
camel-ai/biology
camel-ai/math
metaeval/reclor
openbookqa
mandyyyyii/scibench
derek-thomas/ScienceQA
TIGER-Lab/ScienceEval
jondurbin/airoboros-3.2
LDJnr/Capybara
Cot-Alpaca-GPT4-From-OpenHermes-2.5
STEM-AI-mtl/Electrical-engineering
knowrohit07/saraswati-stem
sablo/oasst2_curated
lmsys/lmsys-chat-1m
TIGER-Lab/MathInstruct
bigbio/med_qa
meta-math/MetaMathQA-40K
openbookqa
piqa
metaeval/reclor
derek-thomas/ScienceQA
scibench
sciq
Open-Orca/SlimOrca
migtissera/Synthia-v1.3
TIGER-Lab/ScienceEval
allenai/WildChat
microsoft/orca-math-word-problems-200k
openchat/openchat_sharegpt4_dataset
teknium/GPTeacher-General-Instruct
m-a-p/CodeFeedback-Filtered-Instruction
totally-not-an-llm/EverythingLM-data-V3
HuggingFaceH4/no_robots
OpenAssistant/oasst_top1_2023-08-25
WizardLM/WizardLM_evol_instruct_70k
abacusai/SystemChat-1.1
H-D-T/Buzz-V1.2 model-index:
name: Einstein-v7-Qwen2-7B 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: 41.0 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v7-Qwen2-7B 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: 32.84 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v7-Qwen2-7B 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: 15.18 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v7-Qwen2-7B 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: 6.6 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v7-Qwen2-7B 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: 14.06 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v7-Qwen2-7B 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: 34.4 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v7-Qwen2-7B name: Open LLM Leaderboard

🔬 Einstein-v7-Qwen2-7B

This model is a full fine-tuned version of Qwen/Qwen2-7B on diverse datasets.

This model is finetuned using 8xMI300X using axolotl.

This model has been trained using compute resources from TensorWave.

See axolotl config

axolotl version: 0.4.0

yaml

base_model: Qwen/Qwen2-7B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

chat_template: chatml
datasets:
  - 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/buzz_unstacked_chosen_math_removed_filtered.json
    ds_type: json
    type: alpaca
    conversation: chatml

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

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

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

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

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

  - path: data/sharegpt_gpt4_english.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/soda_diaolog_longest_tenth_buzz_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/system_conversations_dolphin_sharegpt.json
    ds_type: json
    type: sharegpt
    conversation: chatml
  
dataset_prepared_path: last_run_prepared
val_set_size: 0.002

output_dir: ./Einstein-v7-Qwen2-7B-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:
wandb_log_model:
hub_model_id: Weyaxi/Einstein-v7-Qwen2-7B

gradient_accumulation_steps: 4
micro_batch_size: 6
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.00001 # look

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: unsloth
gradient_checkpointing_kwargs:
   use_reentrant: true # look
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_max_new_tokens: 128
saves_per_epoch: 1
debug:

deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
tokens:
  - "<|im_start|>"
  - "<|im_end|>"

💬 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:

python

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)