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KordAI
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Model Details
Model Provider
KordAI
Model Tree
KordAI/KeawGPT-Base
Input Modalities
Output Modalities
Supported Functionality
KeawGPT is intended for:
It is not intended as a substitute for professional advice (medical, legal, financial) or as an authoritative source without verification — like any language model, it can produce confident-sounding but incorrect output, especially on niche technical claims or citations.
KeawGPT uses a custom plain-text turn format rather than a standard chat markup:
markdown
# SYSTEM:{system message}# USER:{user message}# ASSISTANT:{assistant message}# TOOL:{tool result}
Each assistant turn ends with the model's EOS token. Use tokenizer.apply_chat_template() to render this — a custom chat_template.jinja ships with the tokenizer config and handles turn formatting, the default system message, and tool-schema injection automatically. Building the prompt by hand is unnecessary and error-prone (whitespace/newline placement, tool-schema formatting, etc. are already handled by the template).
KeawGPT can emit structured tool calls instead of a direct answer. When it decides to call a tool, it replies only in this format, with no other content before or after:
markdown
<tools_call><function=tool_name><parameter=param_name>value</parameter></function></tools_call>
The calling application is expected to parse this block, execute the corresponding tool, and feed the result back as a # TOOL: turn before continuing generation. The model may optionally reason in natural language before a tool call, but should not add anything after the closing </tools_call> tag.
Inference with 🤗 Transformers, loaded in 4-bit via bitsandbytes:
python
import reimport jsonimport torchfrom transformers import (AutoModelForCausalLM,AutoTokenizer,BitsAndBytesConfig,TextStreamer,StoppingCriteria,StoppingCriteriaList,)MODEL_NAME = "KordAI/KeawGPT"# --------------------------------------------------------------------------# 1. Load model in 4-bit# --------------------------------------------------------------------------bnb_config = BitsAndBytesConfig(load_in_4bit=True,bnb_4bit_quant_type="nf4",bnb_4bit_compute_dtype=torch.bfloat16,bnb_4bit_use_double_quant=True,)tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)model = AutoModelForCausalLM.from_pretrained(MODEL_NAME,quantization_config=bnb_config,device_map="auto",)model.eval()SYSTEM_PROMPT = None # unused — chat_template.jinja builds the default system# message + tools instruction automatically from `tools`# --------------------------------------------------------------------------# 2. Prompting via tokenizer.apply_chat_template# --------------------------------------------------------------------------# KeawGPT ships with a custom chat_template.jinja (bundled in this repo's# tokenizer_config.json) that renders conversations in the model's native# # SYSTEM: / # USER: / # ASSISTANT: / # TOOL: turn format and injects the# tool schema into the default system message. Just pass `tools=tools` and# let apply_chat_template build the prompt — no manual string building needed.def build_prompt(history):return tokenizer.apply_chat_template(history,tools=tools,tokenize=False,add_generation_prompt=True,)STOP_STRINGS = ["</tool_call>", "</tools_call>", "\n# USER:", "\n# TOOL:"]class StopOnSubstrings(StoppingCriteria):def __init__(self, tokenizer, stop_strings, prompt_len, check_every=4):self.tokenizer = tokenizerself.stop_strings = stop_stringsself.prompt_len = prompt_lenself.check_every = check_everyself._count = 0def __call__(self, input_ids, scores, **kwargs):self._count += 1if self._count % self.check_every != 0:return Falsegenerated = self.tokenizer.decode(input_ids[0][self.prompt_len:], skip_special_tokens=True)return any(s in generated for s in self.stop_strings)def generate(prompt, max_new_tokens=1024):inputs = tokenizer(prompt, return_tensors="pt").to(model.device)prompt_len = inputs["input_ids"].shape[1]streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)stopping_criteria = StoppingCriteriaList([StopOnSubstrings(tokenizer, STOP_STRINGS, prompt_len)])with torch.no_grad():output_ids = model.generate(**inputs,max_new_tokens=max_new_tokens,do_sample=True,temperature=0.7,top_p=0.9,pad_token_id=tokenizer.eos_token_id,streamer=streamer,stopping_criteria=stopping_criteria,)return tokenizer.decode(output_ids[0][prompt_len:], skip_special_tokens=True)# --------------------------------------------------------------------------# 3. Example single-turn call# --------------------------------------------------------------------------history = [{"role": "user", "content": "What's the difference between a P and an NP problem?"}]prompt = build_prompt(history)output = generate(prompt)print(output)
Wrap this in a loop that appends {"role": "assistant", ...} and, when parse_tool_call() returns non-None, dispatches the call, appends the result as a {"role": "tool", ...} turn, and re-generates — this is how tool-calling conversations are extended turn by turn.
<tools_call> blocks and returning results as # TOOL: turns; without that harness, the model may describe a tool call in text without it actually being executed.