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.eval_results/winogrande_et.yaml Normal file
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- dataset:
id: tartuNLP/winogrande_et
task_id: winogrande_et
value: 51.74
date: '2026-02-11'
source:
url: https://huggingface.co/tartuNLP/Llama-3.1-EstLLM-8B-Instruct-0825
name: Model Card
user: adorkin
notes: "3-shot"

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LLAMA 3.1 COMMUNITY LICENSE AGREEMENT
Llama 3.1 Version Release Date: July 23, 2024
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---
library_name: transformers
license: llama3.1
language:
- et
- en
base_model:
- tartuNLP/Llama-3.1-EstLLM-8B-0525
pipeline_tag: text-generation
datasets:
- nvidia/HelpSteer3
- allenai/tulu-3-sft-mixture
- utter-project/EuroBlocks-SFT-Synthetic-1124
---
![image/png](assets/logo-sinine.png)
# Llama 3.1 EstLLM 8B 0825 Instruct
> This checkpoint is identical to [tartuNLP/llama-estllm-prototype-0825](https://huggingface.co/tartuNLP/llama-estllm-prototype-0825). The reupload is for naming consistency in the model tree.
`Llama-3.1-EstLLM-8B-Instruct-0825` is the first artifact produced by the EstLLM project. The intention of this release is to evaluate the first prototype in a conversational
ChatbotArena-style setting on [baromeeter.ai](https://baromeeter.ai), and thus establish a baseline for future improvements.
The model underwent continuous pre-training starting from [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) on approximately 35B tokens,
which resulted in [tartuNLP/Llama-3.1-EstLLM-8B-0525](https://huggingface.co/tartuNLP/Llama-3.1-EstLLM-8B-0525),
then supervised fine-tuning and direct preference optimization were applied.
## Use with transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "tartuNLP/Llama-3.1-EstLLM-8B-Instruct-0825"
model = AutoModelForCausalLM.from_pretrained(
model_name,
dtype="auto",
device_map="auto"
)
# to use on apple silicon, load the following way
# model = AutoModelForCausalLM.from_pretrained(
# model_name,
# dtype=torch.float16,
# device_map="mps",
# )
tokenizer = AutoTokenizer.from_pretrained(model_name)
messages = [
{"role": "user", "content": "Kas sa räägid eesti keelt?"}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer(text, return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=128,
do_sample=True,
temperature=0.4,
# specify eos token to stop at the end of the assistant response
eos_token_id=tokenizer.eos_token_id,
)
# generated_ids include the input tokens as well, so we only decode new tokens
response = tokenizer.decode(
generated_ids[0][model_inputs["input_ids"].shape[1]:],
skip_special_tokens=True,
)
print(response)
```
## Model Details
### Model Description
- **Developed by:** [TartuNLP](https://huggingface.co/tartuNLP) and [TalTechNLP](https://huggingface.co/TalTechNLP) research groups
- **Funded by:** Estonian Ministry of Education and Research, “Estonian Language Technology Program 2018-2027”
- **Model type:** Causal Language Model, Instruction-following
- **Language(s) (NLP):** Estonian, English
- **License:** Llama 3.1 Community License Agreement
- **Finetuned from model:** [tartuNLP/Llama-3.1-EstLLM-8B-0525](https://huggingface.co/tartuNLP/Llama-3.1-EstLLM-8B-0525)
### Continued Pre-Training
Continued Pre-Training was performed for a single epoch on:
- Estonian National Corpus (8.6B tokens)
- Python-Edu (3.3B tokens)
- FineMath4-Plus (9.5B tokens)
- General Instruction-Augmented Corpora (7.4B tokens)
- Cosmopedia v2 (6.9B tokens)
### Supervised Fine-Tuning
Approximately 764k examples were used for Supervised Fine-Tuning. The examples mainly come from [the Tulu 3 SFT mixture](https://huggingface.co/datasets/allenai/tulu-3-sft-mixture) and [EuroBlocks](https://huggingface.co/datasets/utter-project/EuroBlocks-SFT-Synthetic-1124).
Additional data provided by the Institute of Estonian Language (EKI) was also used. In total about 80% of examples are in English. More details TBA.
### Direct Preference Optimization
English-only [HelpSteer3](https://huggingface.co/datasets/nvidia/HelpSteer3) was used as is in the Direct Preference Optimization step, as [previous research on Poro 2 models](https://rocm.blogs.amd.com/artificial-intelligence/multilingual-continued-pretraining/README.html)
showed that there's no observable benefit from translating preference pairs.
## Evaluation
## Logits-based
Scores for logits-based evaluation benchmarks are available on the [EuroEval](https://euroeval.com/leaderboards/Monolingual/estonian/) leaderboard.
## Generative
Every benchmark in this category is treated as a *generative* problem, and thus the evaluation is performed on the model responses obtained with 0 temperature (not logits).
The top scores are higlighted with **bold**. Second best scores are highlighted with **_italic bold_**. Rows are sorted in descending order based on the number of parameters of models (not scores).
The test set is used for evaluation of each dataset unless noted otherwise.
Note that _all models are evaluated with the same prompt template_ for comparability, meaning that the scores do not necessarily represent each model's best possible
performance. This is especially the case for `deepseek-ai/DeepSeek-V3-0324` on some of the benchmarks.
Only models of comparable size are evaluated on benchmarks in English.
### Instruction-following
#### Estonian
Instruction level strict accuracy is reported for IFEval-et.
| Model (# parameters ↓) | [IFEval-et](https://huggingface.co/datasets/tartuNLP/ifeval_et) |
|-------|-----------------------------------|
| moonshotai/Kimi-K2-Instruct | **0.7891** |
| deepseek-ai/DeepSeek-V3.2 | 0.7221 |
| deepseek-ai/DeepSeek-V3-0324 | 0.7171 |
| mistralai/Mistral-Large-3-675B-Instruct-2512 | 0.7097 |
| meta-llama/Llama-3.1-405B-Instruct | 0.7159 |
| meta-llama/Llama-3.3-70B-Instruct | **_0.7705_** |
| Qwen/Qwen2.5-72B-Instruct | 0.7407 |
| google/gemma-3-27b-it | 0.7655 |
| google/gemma-3-12b-it | 0.7556 |
| utter-project/EuroLLM-9B-Instruct | 0.5397 |
| mistralai/Ministral-3-8B-Instruct-2512 | 0.4888 |
| swiss-ai/Apertus-8B-Instruct-2509| 0.5484 |
| meta-llama/Llama-3.1-8B-Instruct | 0.3797 |
| **tartuNLP/Llama-3.1-EstLLM-8B-Instruct-0825** | 0.5174 |
| BSC-LT/salamandra-7b-instruct | 0.5195 |
| tartuNLP/Llammas | 0.3524 |
| Qwen/Qwen2.5-7B-Instruct | 0.4988 |
#### English
Instruction level strict accuracy is reported for IFEval-en.
| Model (# parameters ↓) | [IFEval-en](https://huggingface.co/datasets/tartuNLP/ifeval_en) |
|-------|-----------------------------------|
| utter-project/EuroLLM-9B-Instruct | 0.7004 |
| mistralai/Ministral-3-8B-Instruct-2512 | 0.6845 |
| swiss-ai/Apertus-8B-Instruct-2509 | 0.7808 |
| meta-llama/Llama-3.1-8B-Instruct | **0.8106** |
| **tartuNLP/Llama-3.1-EstLLM-8B-Instruct-0825** | 0.7527 |
| tartuNLP/Llammas | 0.4373 |
| BSC-LT/salamandra-7b-instruct | 0.3289 |
| Qwen/Qwen2.5-7B-Instruct | _**0.7954**_ |
### Multiple Choice
All datasets except Winogrande-et are evaluated in 0-shot mode. Winogrande-et is evaluated in 3-shot mode. Exact match accuracy is reported for every dataset.
#### Estonian Language Competence
| Model (# parameters ↓) | [Grammar-et](https://huggingface.co/datasets/TalTechNLP/grammar_et)| [Inflection-et](https://huggingface.co/datasets/TalTechNLP/inflection_et)| [Word-Meanings-et](https://huggingface.co/datasets/TalTechNLP/word_meanings_et) |
|-------|------|------|--------|
| moonshotai/Kimi-K2-Instruct | **0.916** | 0.6458 | **0.9689** |
| deepseek-ai/DeepSeek-V3.2 | 0.781 | 0.6891 | 0.8134 |
| deepseek-ai/DeepSeek-V3-0324 | 0.364 | 0 | 0 |
| mistralai/Mistral-Large-3-675B-Instruct-2512 | 0.796 | _**0.8355**_ | 0.9488 |
| meta-llama/Llama-3.1-405B-Instruct | **_0.818_** | **0.9089** | 0.9438 |
| meta-llama/Llama-3.3-70B-Instruct | 0.797 | 0.6421 | 0.9408 |
| Qwen/Qwen2.5-72B-Instruct | 0.694 | 0.5208 | 0.9057 |
| google/gemma-3-27b-it | 0.817 | 0.5934 | 0.9529 |
| google/gemma-3-12b-it | 0.789 | 0.4227 | 0.9318 |
| utter-project/EuroLLM-9B-Instruct | 0.764 | 0.367 | 0.9258 |
| mistralai/Ministral-3-8B-Instruct-2512 | 0.562 | 0.4833 | 0.8395 |
| swiss-ai/Apertus-8B-Instruct-2509 | 0.512 | 0.3662 | 0.9027 |
| meta-llama/Llama-3.1-8B-Instruct | 0.657 | 0.4165 | 0.8335 |
| **tartuNLP/Llama-3.1-EstLLM-8B-Instruct-0825** | 0.692 | 0.5188 | **_0.9569_** |
| BSC-LT/salamandra-7b-instruct | 0.594 | 0.2668 | 0.8084 |
| Qwen/Qwen2.5-7B-Instruct | 0.598 | 0.4136 | 0.7984 |
| tartuNLP/Llammas | 0.529 | 0.2289 | 0.5326 |
#### Knowledge and Reasoning (Estonian)
| Model (# parameters ↓) | [Winogrande-et](https://huggingface.co/datasets/tartuNLP/winogrande_et) | [Trivia-et](https://huggingface.co/datasets/TalTechNLP/trivia_et) | [Exam-et](https://huggingface.co/datasets/TalTechNLP/exam_et) | [GlobalPIQA-et](https://huggingface.co/datasets/mrlbenchmarks/global-piqa-nonparallel/viewer/ekk_latn)| [TruthfulQA-et](https://huggingface.co/datasets/LumiOpen/opengpt-x_truthfulqax/viewer/mc_ET) |
|-------|-----------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|-------------------------------------------|
| moonshotai/Kimi-K2-Instruct | **0.8138** | 0.4225 | **0.8414** | **0.79** | **0.7136** |
| deepseek-ai/DeepSeek-V3.2 | 0.4805 | 0.38 | 0.614 | 0.7 | 0.5863 |
| deepseek-ai/DeepSeek-V3-0324 | **_0.8042_** | 0.27 | 0.1221 | 0.04 | 0.2093 |
| mistralai/Mistral-Large-3-675B-Instruct-2512 | 0.7487 | _**0.4275**_ | 0.7931 | _**0.73**_ | 0.6854 |
| meta-llama/Llama-3.1-405B-Instruct |0.7878 | **0.4713** | _**0.8309**_ | 0.58 | _**0.7001**_ |
| meta-llama/Llama-3.3-70B-Instruct |0.7397 | 0.3875 | 0.7652 | 0.58 | 0.6255 |
| Qwen/Qwen2.5-72B-Instruct | 0.7227 | 0.315 | 0.7162 | 0.65 | 0.6683 |
| google/gemma-3-27b-it | 0.7510 | 0.325 | 0.7751 | 0.71 | 0.5814 |
| google/gemma-3-12b-it | 0.6712 | 0.3237 | 0.7069 | 0.54 | 0.3158 |
| utter-project/EuroLLM-9B-Instruct | 0.5846 | 0.3738 | 0.5589 | 0.55 | 0.2889 |
| mistralai/Ministral-3-8B-Instruct-2512 | 0.5812 | 0.3125 | 0.5012 | 0.48 | 0.3525 |
| swiss-ai/Apertus-8B-Instruct-2509 | 0.5105 | 0.345 | 0.552 | 0.59 | 0.366 |
| meta-llama/Llama-3.1-8B-Instruct | 0.5399 | 0.2888 | 0.5 | 0.54 | 0.437 |
| **tartuNLP/Llama-3.1-EstLLM-8B-Instruct-0825** | 0.5812 | 0.425 | 0.5093 | 0.63 | 0.3525 |
| BSC-LT/salamandra-7b-instruct | 0.2878 | 0.2875 | 0.3556 | 0.55 | 0.3011 |
| Qwen/Qwen2.5-7B-Instruct | 0.5473 | 0.2938 | 0.4913 | 0.57 | 0.4113 |
| tartuNLP/Llammas | 0.5037 | 0.2838 | 0.3649 | 0.01 | 0.2032 |
#### Knowledge and Reasoning (English)
| Model (# parameters ↓) | [Winogrande](https://huggingface.co/datasets/allenai/winogrande) | [GlobalPIQA-en](https://huggingface.co/datasets/mrlbenchmarks/global-piqa-nonparallel/viewer/eng_latn) | [TruthfulQA](https://huggingface.co/datasets/truthfulqa/truthful_qa) | [MMLU-Redux](https://huggingface.co/datasets/edinburgh-dawg/mmlu-redux-2.0) | [GSM8K](https://huggingface.co/datasets/openai/gsm8k) |
|-------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|
| utter-project/EuroLLM-9B-Instruct | 0.5059 | 0.58 | 0.2962 | 0.5741 | 0.5944 |
| meta-llama/Llama-3.1-8B-Instruct | 0.5625 | 0.76 | _**0.5239**_ | 0.6959 | _**0.7710**_ |
| mistralai/Ministral-3-8B-Instruct-2512 | _**0.6503**_ | _**0.77**_ | 0.519 | _**0.7418**_ | 0.3927 |
| swiss-ai/Apertus-8B-Instruct-2509 | 0.5133 | 0.73 | 0.3831 | 0.6099 | 0.5936 |
| **tartuNLP/Llama-3.1-EstLLM-8B-Instruct-0825** | 0.6084 | 0.71 | 0.366 | 0.6388 | 0.7202 |
| tartuNLP/Llammas | 0.498 | 0 | 0.1971 | 0.3417 | 0.1456 |
| BSC-LT/salamandra-7b-instruct | 0.4029 | 0.63 | 0.2717 | 0.5180 | 0.0076 |
| Qwen/Qwen2.5-7B-Instruct | **0.6627** | **0.83** | **0.5875** | **0.7555** | **0.7862** |
### Translation
#### English to Estonian
| Model | [wmt24pp](https://huggingface.co/datasets/google/wmt24pp) (BLEU ↑) |
|-------|---------|
| BSC-LT/salamandraTA-7b-instruct | 0.2713 |
| **tartuNLP/Llama-3.1-EstLLM-8B-Instruct-0825** | 0.264 |
| utter-project/EuroLLM-9B-Instruct | 0.2602 |
| swiss-ai/Apertus-8B-Instruct-2509 | 0.2372 |
| tartuNLP/Llammas | 0.1472 |
| meta-llama/Llama-3.1-8B-Instruct | 0.1406 |
| BSC-LT/salamandra-7b-instruct | 0.1201 |
| Qwen/Qwen2.5-7B-Instruct | 0.0476 |
## Limitations
This is an early prototype version. Accordingly, it has limitations *in addition* to the base Llama limitations:
- Relatively short context of 4096 tokens. It's not expected to perform well on context sizes beyond that.
- Multi-turn conversations are not supported in this version.
- Trained with the original Llama 3.1 system prompt that has a hard-coded date cut-off.
## Citation
```
@misc{dorkin2026estllmenhancingestoniancapabilities,
title={{EstLLM: Enhancing Estonian Capabilities in Multilingual LLMs via Continued Pretraining and Post-Training}},
author={Aleksei Dorkin and Taido Purason and Emil Kalbaliyev and Hele-Andra Kuulmets and Marii Ojastu and Mark Fišel and Tanel Alumäe and Eleri Aedmaa and Krister Kruusmaa and Kairit Sirts},
year={2026},
eprint={2603.02041},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2603.02041},
}
```

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# Llama 3.1 Acceptable Use Policy
Meta is committed to promoting safe and fair use of its tools and features, including Llama 3.1. If you
access or use Llama 3.1, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of
this policy can be found at [https://llama.meta.com/llama3_1/use-policy](https://llama.meta.com/llama3_1/use-policy)
## Prohibited Uses
We want everyone to use Llama 3.1 safely and responsibly. You agree you will not use, or allow
others to use, Llama 3.1 to:
1. Violate the law or others’ rights, including to:
1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
1. Violence or terrorism
2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
3. Human trafficking, exploitation, and sexual violence
4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
5. Sexual solicitation
6. Any other criminal activity
3. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
4. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
5. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
6. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
7. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials
8. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 3.1 related to the following:
1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
2. Guns and illegal weapons (including weapon development)
3. Illegal drugs and regulated/controlled substances
4. Operation of critical infrastructure, transportation technologies, or heavy machinery
5. Self-harm or harm to others, including suicide, cutting, and eating disorders
6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
3. Intentionally deceive or mislead others, including use of Llama 3.1 related to the following:
1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
3. Generating, promoting, or further distributing spam
4. Impersonating another individual without consent, authorization, or legal right
5. Representing that the use of Llama 3.1 or outputs are human-generated
6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
4. Fail to appropriately disclose to end users any known dangers of your AI system
Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation
of this Policy through one of the following means:
* Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://github.com/meta-llama/llama-models/issues)
* Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback
* Reporting bugs and security concerns: facebook.com/whitehat/info
* Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.1: LlamaUseReport@meta.com

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{{- bos_token }}
{%- if custom_tools is defined %}
{%- set tools = custom_tools %}
{%- endif %}
{%- if not tools_in_user_message is defined %}
{%- set tools_in_user_message = true %}
{%- endif %}
{%- if not date_string is defined %}
{%- set date_string = "26 Jul 2024" %}
{%- endif %}
{%- if not tools is defined %}
{%- set tools = none %}
{%- endif %}
{#- This block extracts the system message, so we can slot it into the right place. #}
{%- if messages[0]['role'] == 'system' %}
{%- set system_message = messages[0]['content']|trim %}
{%- set messages = messages[1:] %}
{%- else %}
{%- set system_message = "" %}
{%- endif %}
{#- System message + builtin tools #}
{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
{%- if builtin_tools is defined or tools is not none %}
{{- "Environment: ipython\n" }}
{%- endif %}
{%- if builtin_tools is defined %}
{{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
{%- endif %}
{{- "Cutting Knowledge Date: December 2023\n" }}
{{- "Today Date: " + date_string + "\n\n" }}
{%- if tools is not none and not tools_in_user_message %}
{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
{{- "Do not use variables.\n\n" }}
{%- for t in tools %}
{{- t | tojson(indent=4) }}
{{- "\n\n" }}
{%- endfor %}
{%- endif %}
{{- system_message }}
{{- "<|eot_id|>" }}
{#- Custom tools are passed in a user message with some extra guidance #}
{%- if tools_in_user_message and not tools is none %}
{#- Extract the first user message so we can plug it in here #}
{%- if messages | length != 0 %}
{%- set first_user_message = messages[0]['content']|trim %}
{%- set messages = messages[1:] %}
{%- else %}
{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
{%- endif %}
{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
{{- "Given the following functions, please respond with a JSON for a function call " }}
{{- "with its proper arguments that best answers the given prompt.\n\n" }}
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
{{- "Do not use variables.\n\n" }}
{%- for t in tools %}
{{- t | tojson(indent=4) }}
{{- "\n\n" }}
{%- endfor %}
{{- first_user_message + "<|eot_id|>"}}
{%- endif %}
{%- for message in messages %}
{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
{%- elif 'tool_calls' in message %}
{%- if not message.tool_calls|length == 1 %}
{{- raise_exception("This model only supports single tool-calls at once!") }}
{%- endif %}
{%- set tool_call = message.tool_calls[0].function %}
{%- if builtin_tools is defined and tool_call.name in builtin_tools %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
{{- "<|python_tag|>" + tool_call.name + ".call(" }}
{%- for arg_name, arg_val in tool_call.arguments | items %}
{{- arg_name + '="' + arg_val + '"' }}
{%- if not loop.last %}
{{- ", " }}
{%- endif %}
{%- endfor %}
{{- ")" }}
{%- else %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
{{- '{"name": "' + tool_call.name + '", ' }}
{{- '"parameters": ' }}
{{- tool_call.arguments | tojson }}
{{- "}" }}
{%- endif %}
{%- if builtin_tools is defined %}
{#- This means we're in ipython mode #}
{{- "<|eom_id|>" }}
{%- else %}
{{- "<|eot_id|>" }}
{%- endif %}
{%- elif message.role == "tool" or message.role == "ipython" %}
{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
{%- if message.content is mapping or message.content is iterable %}
{{- message.content | tojson }}
{%- else %}
{{- message.content }}
{%- endif %}
{{- "<|eot_id|>" }}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
{%- endif %}

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{
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"LlamaForCausalLM"
],
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"eos_token_id": 128001,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 131072,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": {
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"high_freq_factor": 4.0,
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"original_max_position_embeddings": 8192,
"rope_type": "llama3"
},
"rope_theta": 500000.0,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.53.1",
"use_cache": false,
"vocab_size": 128256
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