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---
license: mit
language:
- en
base_model:
- Qwen/Qwen3-0.6B
library_name: transformers
tags:
- not-for-all-audiences
- nsfw
- 4chan
- uncensored
---
# Chan-0.6B Model Card
## Model Overview
**Model Name:** Chan-0.6B
**Version:** 1.0
**Model Type:** Transformer language model
**Parameter Count:** ~600 M
**Base Model:** Qwen‑3‑0.6 B (float16)
## Training Data
**Training Data:** ~200 M tokens extracted from 4 Chan posts (public discussion boards)
**Training Compute:** 1 × NVIDIA RTX 3090 GPU (FP16) for ~7 days
## Intended Use
Chan-0.6B is a chatbot trained on informal internet dialogue. It is suitable for:
* Low‑cost prototyping of conversational agents.
* Academic research into fine‑tuning on noisy dialogue data.
* Exploration of 4 Chan‑style language in controlled settings.
**Not Intended For:**
* Commercial customer‑facing deployments.
* Moderation or content‑sensitive environments.
* Use cases where safe, neutral, or factually accurate output is required.
## Preprocessing
* No special filtering of offensive words.
* Long posts truncated to 1024 tokens (with special token).
* No deduplication or filtering for content quality.
## Limitations & Known Issues
* **Toxicity:** The model inherits and can amplify offensive language from the 4 Chan corpus.
* **Content Bias:** Strongly biased toward the linguistic style, slang, and viewpoints present on 4 Chan.
* **Fact-Checking:** Not trained on structured knowledge, outputs may contain hallucinations.
* **Safety:** No reinforcement‑learning‑from‑human‑feedback or safe‑alignment training was performed.
* **Performance:** Slower than smaller chat models due to 600 M parameters; requires a GPU for real-time inference.
## Usage
```
import sys
import gc
import atexit
from pathlib import Path
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from flask import Flask, request, jsonify
from better_profanity import profanity
# ------------------ Config ------------------
CHECKPOINT_DIR = Path("./XL_V2/checkpoint-epoch3").absolute()
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
MAX_LEN = 1024 * 2
MAX_NEW_TOKENS = 256 * 2
TEMPERATURE = 0.7
TOP_P = 0.9
TOP_K = 50
REPETITION_PENALTY = 1.18
# Default censor setting (can be overridden per request)
CENSOR = False
profanity.load_censor_words()
# ------------------ Globals ------------------
history = []
# ------------------ Load model ------------------
print(f"[{DEVICE}] Loading tokenizer & model from {CHECKPOINT_DIR} …")
tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT_DIR)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.model_max_length = MAX_LEN
model = AutoModelForCausalLM.from_pretrained(
CHECKPOINT_DIR,
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
).to(DEVICE)
# ------------------ Cleanup on exit ------------------
def cleanup():
global model, tokenizer
print("[exit] Cleaning up resources...")
if "model" in globals():
del model
if "tokenizer" in globals():
del tokenizer
gc.collect()
if torch.cuda.is_available():
torch.cuda.empty_cache()
atexit.register(cleanup)
# ------------------ Helpers ------------------
def truncate_tokens(encoded, max_len):
"""Left-truncate tokenized input to keep newest tokens"""
ids = encoded["input_ids"][0]
if ids.size(0) <= max_len:
return encoded
attn = torch.ones_like(ids[-max_len:])
return {"input_ids": ids[-max_len:].unsqueeze(0), "attention_mask": attn.unsqueeze(0)}
def build_prompt(history):
"""Construct conversation in dataset format with trailing Assistant cue"""
lines = []
for msg in history:
role = "User" if msg["role"] == "user" else "Assistant"
lines.append(f"{role}: {msg['content']}")
lines.append("Assistant: ")
return "\n".join(lines)
def generate_response(append_last_user=True, censor_override=None) -> str:
"""
Generate text like reg script:
- Uses global history
- Left-truncates context
- Extracts Assistant reply
- Removes <|endoftext|>
- Anti-echo patches
- Optional profanity censor ONLY for AI reply
"""
global history
if not history or history[-1]["role"] != "user":
return ""
prompt_text = history[-1]["content"]
if append_last_user and (not history or history[-1]["role"] != "user"):
history.append({"role": "user", "content": prompt_text})
prompt = build_prompt(history)
#print(prompt)
tokens = tokenizer(prompt, return_tensors="pt", truncation=False)
tokens = truncate_tokens(tokens, MAX_LEN)
tokens = {k: v.to(DEVICE) for k, v in tokens.items()}
out_ids = model.generate(
**tokens,
max_new_tokens=MAX_NEW_TOKENS,
min_new_tokens=80,
do_sample=True,
temperature=TEMPERATURE,
top_p=TOP_P,
top_k=TOP_K,
repetition_penalty=REPETITION_PENALTY,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
)
decoded = tokenizer.decode(out_ids[0], skip_special_tokens=False)
decoded = decoded.replace("<|endoftext|>", "").strip()
if "Assistant:" in decoded:
assistant_reply = decoded.rsplit("Assistant:", 1)[1].strip()
else:
assistant_reply = decoded.strip()
# Anti-echo
last_user = history[-1]["content"]
if assistant_reply.startswith(last_user):
assistant_reply = assistant_reply[len(last_user):].lstrip()
if last_user[:20] in assistant_reply[:60]:
assistant_reply = assistant_reply.replace(last_user, "").strip()
if assistant_reply.startswith(">") and last_user[:20] in assistant_reply:
assistant_reply = assistant_reply.split("\n", 1)[-1].strip()
assistant_reply = assistant_reply.strip()
# Append raw (UNCENSORED) assistant reply to history
history.append({"role": "assistant", "content": assistant_reply})
# Apply censor only to returned text, not stored history
do_censor = CENSOR if censor_override is None else bool(censor_override)
if do_censor:
return profanity.censor(assistant_reply)
return assistant_reply
# ------------------ Flask app ------------------
app = Flask(__name__)
@app.route("/generate", methods=["POST"])
def generate_endpoint():
global history
prompt = ""
censor_flag = None
try:
data = request.get_json(silent=True)
#print(data)
if isinstance(data, dict):
censor_flag = data.get("censor", None)
# ChatML-like messages mode
if "messages" in data and isinstance(data["messages"], list):
history = []
for msg in data["messages"]:
if "role" in msg and "content" in msg:
history.append({"role": msg["role"], "content": msg["content"]})
return jsonify({
"response": generate_response(
append_last_user=False,
censor_override=censor_flag
)
})
# simple prompt mode
prompt = str(data.get("prompt", "")).strip()
elif isinstance(data, str):
prompt = data.strip()
except Exception:
pass
if not prompt:
prompt = request.data.decode("utf-8").strip()
if not prompt:
return jsonify({"error": "Missing prompt"}), 400
# Add user message
history.append({"role": "user", "content": prompt})
# Generate response
try:
answer = generate_response(
append_last_user=False,
censor_override=censor_flag
)
return jsonify({"response": answer})
except Exception as exc:
sys.stderr.write(str(exc) + "\n")
return jsonify({"error": "Internal server error"}), 500
@app.route("/health", methods=["GET"])
def health():
return jsonify({"status": "ok"})
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Run Flask LLM API")
parser.add_argument("--host", default="0.0.0.0", help="Hostname")
parser.add_argument("--port", type=int, default=8000, help="Port")
args = parser.parse_args()
app.run(host=args.host, port=args.port)
```
**Hardware:**
* Requires at least a single 12 GB GPU for inference. CPU inference is possible but slow.
**Safety:**
* We strongly recommend applying a toxicity filter or reinforcement learning-based safety policy before deployment.
## License
MIT (see [LICENSE file](LICENSE))
## Disclaimer
This model was trained on user-generated internet content that includes hate speech, harassment, and extremist viewpoints. The creators do not endorse or support these views. The model should be used responsibly, with proper safety safeguards, and in compliance with all local laws and platform policies.

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"<|vision_start|>": 151652
}

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{%- 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 %}

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{
"architectures": [
"Qwen3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 151643,
"dtype": "float32",
"eos_token_id": 151643,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
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],
"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,
"transformers_version": "4.57.1",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151669
}

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{
"bos_token_id": 151643,
"eos_token_id": 151643,
"max_new_tokens": 2048,
"transformers_version": "4.57.1"
}

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{
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
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"<|video_pad|>"
],
"eos_token": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"pad_token": "<|endoftext|>"
}

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{
"add_bos_token": false,
"add_prefix_space": false,
"added_tokens_decoder": {
"151643": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
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"single_word": false,
"special": true
},
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"special": true
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"lstrip": false,
"normalized": false,
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"lstrip": false,
"normalized": false,
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"lstrip": false,
"normalized": false,
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"single_word": false,
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"151659": {
"content": "<|fim_prefix|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
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"151660": {
"content": "<|fim_middle|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151661": {
"content": "<|fim_suffix|>",
"lstrip": false,
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"rstrip": false,
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"151662": {
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"lstrip": false,
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"151663": {
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"lstrip": false,
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"151668": {
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"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
}
},
"additional_special_tokens": [
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"<|im_end|>",
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"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
"<|vision_start|>",
"<|vision_end|>",
"<|vision_pad|>",
"<|image_pad|>",
"<|video_pad|>"
],
"bos_token": null,
"clean_up_tokenization_spaces": false,
"eos_token": "<|endoftext|>",
"errors": "replace",
"extra_special_tokens": {},
"model_max_length": 1024,
"pad_token": "<|endoftext|>",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

1
vocab.json Normal file
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