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---
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language:
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- tr
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- en
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license: apache-2.0
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tags:
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- turkish
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- turkish-llm
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- turkish-nlp
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- base-model
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- neuroturk
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- hyz01
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- text-generation
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- cpt
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- qwen3
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- 4-bit-precision
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- gguf
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- quantized
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- pytorch
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library_name: transformers
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pipeline_tag: text-generation
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---
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<div align="center">
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**Open-source base language model pre-trained for Turkish by NeuroTürk**
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[](LICENSE)
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[]()
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[](https://huggingface.co/NeuroTurk)
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[](https://github.com/neuroturk)
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[](https://x.com/neuroturk_ai)
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</div>
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---
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## 1. Introduction
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HYZ-01-0.6B-Base is the **base (pre-trained only) version** of the HYZ-01 series developed by **NeuroTürk**. It is a raw language model that has undergone multi-stage Turkish continual pre-training (CPT) on top of a multilingual foundation, without any instruction tuning or alignment. It is intended for researchers and developers who wish to fine-tune the model for their own tasks.
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The model is built on a multilingual foundation covering 119 languages and has been continuously pre-trained with a focus on Turkish. The tokenizer has been extended specifically for Turkish morphological structure and advanced use cases. **HYZ-01-0.6B-Base is the lightweight, open-source base version of HYZ-01, developed by NeuroTürk for Turkish.**
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> Note: This is the base pre-trained version. For the instruction-tuned version, see: [HYZ-01-0.6B](https://huggingface.co/neuroturk/HYZ-01-0.6B)
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---
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## 2. Model Summary
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### Continual Pre-Training
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- **Base model:** 4-stage Turkish continual pre-training (CPT) applied on top of a multilingual foundation.
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- Training stages include general Turkish web corpus, curated domain data, Wikipedia, and high-quality filtered text.
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- Optimization: bfloat16, flash-attention-2, AdamW.
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### Tokenizer Extension
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New special tokens were added to the tokenizer for two purposes:
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- **Language-structure tokens:** To represent Turkish morphological features more efficiently.
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- **Task and structure tokens:** To support structural use cases such as chain-of-thought, code blocks, section markers, and language labels.
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The following 20 tokens have been added to the vocabulary and are reserved as infrastructure for future advanced capabilities:
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| Group | Tokens | Future Use |
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|:---|:---|:---|
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| Brand | `<\|neuroturk\|>` `<\|hyz01\|>` `<\|tr\|>` `<\|en\|>` | Model identity and multilingual control |
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| Chain-of-Thought | `<\|think\|>` `<\|/think\|>` `<\|step\|>` `<\|answer\|>` | Step-by-step reasoning (CoT) |
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| Dialogue | `<\|system\|>` `<\|user\|>` `<\|assistant\|>` `<\|end\|>` | Multi-turn dialogue and role management |
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| Code | `<\|code\|>` `<\|/code\|>` `<\|output\|>` `<\|error\|>` | Structured code generation and debugging |
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| Structure | `<\|title\|>` `<\|section\|>` `<\|list\|>` `<\|note\|>` | Long-form and structured text generation (reports, articles, etc.) |
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---
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## 3. Model Details
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| Feature | Value |
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|:---|:---|
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| Total parameters | 595,798,016 (~0.6B) |
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| Non-embedding parameters | 440,467,456 (~0.44B) |
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| Hidden dimension | 1,024 |
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| Number of layers | 28 |
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| Attention heads (Q) | 16 |
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| Attention heads (KV) | 8 (GQA) |
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| Head dimension | 128 |
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| Activation | SiLU |
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| Normalization | RMSNorm (ε = 1 × 10⁻⁶) |
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| Positional encoding | RoPE (θ = 1,000,000) |
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| Vocabulary size | 151,690 |
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| Training context length | 4,096 tokens |
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| Theoretical max context | 32,768 tokens |
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| Precision | BFloat16 |
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| VRAM usage (fp16) | ~1.11 GB |
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||||||
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| Disk size | ~1.11 GB |
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||||||
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||||||
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---
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## 4. Training Details
|
||||||
|
|
||||||
|
| Setting | Value |
|
||||||
|
|---|---|
|
||||||
|
| Training type | Continual Pre-Training (CPT) |
|
||||||
|
| Number of stages | 4 |
|
||||||
|
| Optimization | AdamW |
|
||||||
|
| Precision | BFloat16 |
|
||||||
|
| LR schedule | Cosine with warmup |
|
||||||
|
| Context length | 4,096 tokens |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 5. Usage
|
||||||
|
|
||||||
|
> **Warning:** This is a base model. It is not instruction-tuned and will not follow instructions reliably. For conversational or task-oriented use, use the instruction-tuned version: [HYZ-01-0.6B](https://huggingface.co/neuroturk/HYZ-01-0.6B)
|
||||||
|
|
||||||
|
### Installation
|
||||||
|
|
||||||
|
```bash
|
||||||
|
pip install transformers torch accelerate
|
||||||
|
```
|
||||||
|
|
||||||
|
### Text Generation (Completion)
|
||||||
|
|
||||||
|
```python
|
||||||
|
from transformers import AutoTokenizer, AutoModelForCausalLM
|
||||||
|
import torch
|
||||||
|
|
||||||
|
model_name = "neuroturk/HYZ-01-0.6B-Base"
|
||||||
|
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained(
|
||||||
|
model_name,
|
||||||
|
trust_remote_code=True,
|
||||||
|
fix_mistral_regex=True
|
||||||
|
)
|
||||||
|
model = AutoModelForCausalLM.from_pretrained(
|
||||||
|
model_name,
|
||||||
|
torch_dtype=torch.bfloat16,
|
||||||
|
device_map="auto",
|
||||||
|
)
|
||||||
|
|
||||||
|
prompt = "Yapay zeka, bilgisayar sistemlerinin"
|
||||||
|
|
||||||
|
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
|
||||||
|
|
||||||
|
outputs = model.generate(
|
||||||
|
**inputs,
|
||||||
|
max_new_tokens=200,
|
||||||
|
temperature=0.8,
|
||||||
|
top_p=0.95,
|
||||||
|
do_sample=True,
|
||||||
|
repetition_penalty=1.1,
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
new_tokens = outputs[0][inputs['input_ids'].shape[1]:]
|
||||||
|
print(tokenizer.decode(new_tokens, skip_special_tokens=True))
|
||||||
|
```
|
||||||
|
|
||||||
|
### Low VRAM (4-bit Quantization)
|
||||||
|
|
||||||
|
```python
|
||||||
|
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
|
||||||
|
import torch
|
||||||
|
|
||||||
|
bnb_config = BitsAndBytesConfig(
|
||||||
|
load_in_4bit=True,
|
||||||
|
bnb_4bit_compute_dtype=torch.bfloat16,
|
||||||
|
bnb_4bit_use_double_quant=True,
|
||||||
|
bnb_4bit_quant_type="nf4",
|
||||||
|
)
|
||||||
|
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained(
|
||||||
|
"neuroturk/HYZ-01-0.6B-Base",
|
||||||
|
trust_remote_code=True,
|
||||||
|
)
|
||||||
|
model = AutoModelForCausalLM.from_pretrained(
|
||||||
|
"neuroturk/HYZ-01-0.6B-Base",
|
||||||
|
quantization_config=bnb_config,
|
||||||
|
device_map="auto",
|
||||||
|
)
|
||||||
|
```
|
||||||
|
|
||||||
|
### Fine-Tuning with Unsloth
|
||||||
|
|
||||||
|
```python
|
||||||
|
from unsloth import FastLanguageModel
|
||||||
|
|
||||||
|
model, tokenizer = FastLanguageModel.from_pretrained(
|
||||||
|
model_name="neuroturk/HYZ-01-0.6B-Base",
|
||||||
|
max_seq_length=4096,
|
||||||
|
load_in_4bit=True,
|
||||||
|
)
|
||||||
|
|
||||||
|
model = FastLanguageModel.get_peft_model(
|
||||||
|
model,
|
||||||
|
r=32,
|
||||||
|
lora_alpha=64,
|
||||||
|
lora_dropout=0.0,
|
||||||
|
target_modules=[
|
||||||
|
"q_proj", "k_proj", "v_proj", "o_proj",
|
||||||
|
"gate_proj", "up_proj", "down_proj",
|
||||||
|
],
|
||||||
|
use_gradient_checkpointing="unsloth",
|
||||||
|
)
|
||||||
|
```
|
||||||
|
---
|
||||||
|
|
||||||
|
### GGUF Quantizations
|
||||||
|
|
||||||
|
For faster inference and lower resource usage, GGUF quantized versions of HYZ-01-0.6B-Base are available. These were kindly provided by [mradermacher](https://huggingface.co/mradermacher).
|
||||||
|
|
||||||
|
You can find them here: [__HYZ-01-0.6B-Base-GGUF__](https://huggingface.co/mradermacher/HYZ-01-0.6B-Base-GGUF)
|
||||||
|
|
||||||
|
**Using with llama.cpp**
|
||||||
|
|
||||||
|
1. Download the GGUF file (e.g., `hyz-01-0.6b-base-q4_k_m.gguf`) from the repository above.
|
||||||
|
2. Run with `llama.cpp`:
|
||||||
|
```bash
|
||||||
|
./main -m hyz-01-0.6b-base-q4_k_m.gguf -p "Your prompt here" -n 512
|
||||||
|
For a detailed explanation of quantization types (e.g., Q4_K_M, Q5_K_M), see the llama.cpp documentation.
|
||||||
|
|
||||||
|
> Note: These GGUF files are not officially maintained by NeuroTürk, but they are community-tested and widely used. Thanks again to mradermacher for the contribution.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 6. Limitations
|
||||||
|
|
||||||
|
- This is a base model without instruction tuning — it will not follow instructions reliably.
|
||||||
|
- Complex multi-step reasoning may be limited with 0.6B parameters.
|
||||||
|
- Biases present in the training data may be reflected in outputs.
|
||||||
|
- Performance drops significantly in languages other than Turkish.
|
||||||
|
- Human verification of outputs is recommended for critical applications.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 7. Citation
|
||||||
|
|
||||||
|
```bibtex
|
||||||
|
@misc{neuroturk2026hyz01,
|
||||||
|
author = {NeuroTürk},
|
||||||
|
title = {HYZ-01-0.6B: A Lightweight Turkish Base Model},
|
||||||
|
year = 2026,
|
||||||
|
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
<div align="center">
|
||||||
|
<sub>NeuroTürk · HYZ01 · 2026</sub>
|
||||||
|
</div>
|
||||||
85
chat_template.jinja
Normal file
85
chat_template.jinja
Normal file
@@ -0,0 +1,85 @@
|
|||||||
|
{%- if tools %}
|
||||||
|
{{- '<|im_start|>system\n' }}
|
||||||
|
{%- if messages[0].role == 'system' %}
|
||||||
|
{{- messages[0].content + '\n\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
||||||
|
{%- for tool in tools %}
|
||||||
|
{{- "\n" }}
|
||||||
|
{{- tool | tojson }}
|
||||||
|
{%- endfor %}
|
||||||
|
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||||
|
{%- else %}
|
||||||
|
{%- if messages[0].role == 'system' %}
|
||||||
|
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
|
||||||
|
{%- for message in messages[::-1] %}
|
||||||
|
{%- set index = (messages|length - 1) - loop.index0 %}
|
||||||
|
{%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
|
||||||
|
{%- set ns.multi_step_tool = false %}
|
||||||
|
{%- set ns.last_query_index = index %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endfor %}
|
||||||
|
{%- for message in messages %}
|
||||||
|
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
||||||
|
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||||
|
{%- elif message.role == "assistant" %}
|
||||||
|
{%- set content = message.content %}
|
||||||
|
{%- set reasoning_content = '' %}
|
||||||
|
{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
|
||||||
|
{%- set reasoning_content = message.reasoning_content %}
|
||||||
|
{%- else %}
|
||||||
|
{%- if '</think>' in message.content %}
|
||||||
|
{%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
|
||||||
|
{%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- if loop.index0 > ns.last_query_index %}
|
||||||
|
{%- if loop.last or (not loop.last and reasoning_content) %}
|
||||||
|
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
||||||
|
{%- else %}
|
||||||
|
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- else %}
|
||||||
|
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- if message.tool_calls %}
|
||||||
|
{%- for tool_call in message.tool_calls %}
|
||||||
|
{%- if (loop.first and content) or (not loop.first) %}
|
||||||
|
{{- '\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- if tool_call.function %}
|
||||||
|
{%- set tool_call = tool_call.function %}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '<tool_call>\n{"name": "' }}
|
||||||
|
{{- tool_call.name }}
|
||||||
|
{{- '", "arguments": ' }}
|
||||||
|
{%- if tool_call.arguments is string %}
|
||||||
|
{{- tool_call.arguments }}
|
||||||
|
{%- else %}
|
||||||
|
{{- tool_call.arguments | tojson }}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '}\n</tool_call>' }}
|
||||||
|
{%- endfor %}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '<|im_end|>\n' }}
|
||||||
|
{%- elif message.role == "tool" %}
|
||||||
|
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
||||||
|
{{- '<|im_start|>user' }}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '\n<tool_response>\n' }}
|
||||||
|
{{- message.content }}
|
||||||
|
{{- '\n</tool_response>' }}
|
||||||
|
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||||
|
{{- '<|im_end|>\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endfor %}
|
||||||
|
{%- if add_generation_prompt %}
|
||||||
|
{{- '<|im_start|>assistant\n' }}
|
||||||
|
{%- if enable_thinking is defined and enable_thinking is false %}
|
||||||
|
{{- '<think>\n\n</think>\n\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
32
config.json
Normal file
32
config.json
Normal file
@@ -0,0 +1,32 @@
|
|||||||
|
{
|
||||||
|
"architectures": [
|
||||||
|
"Qwen3ForCausalLM"
|
||||||
|
],
|
||||||
|
"attention_bias": false,
|
||||||
|
"attention_dropout": 0.0,
|
||||||
|
"bos_token_id": 151643,
|
||||||
|
"eos_token_id": 151643,
|
||||||
|
"head_dim": 128,
|
||||||
|
"hidden_act": "silu",
|
||||||
|
"hidden_size": 1024,
|
||||||
|
"initializer_range": 0.02,
|
||||||
|
"intermediate_size": 3072,
|
||||||
|
"max_position_embeddings": 32768,
|
||||||
|
"max_window_layers": 28,
|
||||||
|
"model_type": "qwen3",
|
||||||
|
"num_attention_heads": 16,
|
||||||
|
"num_hidden_layers": 28,
|
||||||
|
"num_key_value_heads": 8,
|
||||||
|
"rms_norm_eps": 1e-06,
|
||||||
|
"rope_scaling": null,
|
||||||
|
"rope_theta": 1000000,
|
||||||
|
"sliding_window": null,
|
||||||
|
"tie_word_embeddings": true,
|
||||||
|
"torch_dtype": "bfloat16",
|
||||||
|
"use_cache": true,
|
||||||
|
"use_sliding_window": false,
|
||||||
|
"vocab_size": 151689,
|
||||||
|
"_name_or_path": "neuroturk/HYZ-01-0.6B-Base",
|
||||||
|
"model_name": "HYZ-01-0.6B-Base",
|
||||||
|
"name_or_path": "NeuroTürk/HYZ-01-0.6B-Base"
|
||||||
|
}
|
||||||
6
generation_config.json
Normal file
6
generation_config.json
Normal file
@@ -0,0 +1,6 @@
|
|||||||
|
{
|
||||||
|
"bos_token_id": 151643,
|
||||||
|
"do_sample": false,
|
||||||
|
"eos_token_id": 151643,
|
||||||
|
"max_new_tokens": 2048
|
||||||
|
}
|
||||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:d87f5eca41cdc32f10564a5812dfaefc6d161047a2964dc2e75671282715390f
|
||||||
|
size 1191629240
|
||||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:4413ca9699f9f98f1bfc05ebc1536aedd3f276901f3e95640892e6d2378f77a5
|
||||||
|
size 11426375
|
||||||
19
tokenizer_config.json
Normal file
19
tokenizer_config.json
Normal file
@@ -0,0 +1,19 @@
|
|||||||
|
{
|
||||||
|
"add_prefix_space": false,
|
||||||
|
"backend": "tokenizers",
|
||||||
|
"bos_token": null,
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|endoftext|>",
|
||||||
|
"errors": "replace",
|
||||||
|
"is_local": true,
|
||||||
|
"model_max_length": 32768,
|
||||||
|
"model_name": "NeuroTurk/HYZ-01-0.6B-Base",
|
||||||
|
"organization": "NeuroTürk",
|
||||||
|
"pad_token": "<|vision_pad|>",
|
||||||
|
"padding_side": "right",
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "TokenizersBackend",
|
||||||
|
"unk_token": null,
|
||||||
|
"_name_or_path": "neuroturk/HYZ-01-0.6B-Base",
|
||||||
|
"name_or_path": "NeuroTürk/HYZ-01-0.6B-Base"
|
||||||
|
}
|
||||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user