Orchestrator-8B-Q8_0-GGUF

AXONVERTEX-AI-RESEARCH/Orchestrator-8B-Q8_0-GGUF

This model was converted to GGUF format from nvidia/Orchestrator-8B using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with ollama

root@90dd7d73d62b:/# ollama pull hf.co/AXONVERTEX-AI-RESEARCH/Qwen3-Embedding-0.6B-Q8_0-GGUF:Q8_0
pulling manifest 
pulling ee029816fb96: 100% ▕██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ 639 MB                         
pulling eb4402837c78: 100% ▕██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ 1.5 KB                         
pulling 4a6ce91d86a8: 100% ▕██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏   99 B                         
pulling be570f0686c3: 100% ▕██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏  549 B                         
verifying sha256 digest 
writing manifest 
success 
root@90dd7d73d62b:/# ollama pull hf.co/AXONVERTEX-AI-RESEARCH/Orchestrator-8B-Q8_0-GGUF:Q8_0
pulling manifest 
pulling 7ba8f19c5542: 100% ▕██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ 8.7 GB                         
pulling eb4402837c78: 100% ▕██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ 1.5 KB                         
pulling 4a6ce91d86a8: 100% ▕██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏   99 B                         
pulling 9dfdfd94d3aa: 100% ▕██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏  552 B                         
verifying sha256 digest 
writing manifest 
success 
root@90dd7d73d62b:/# ollama run  hf.co/AXONVERTEX-AI-RESEARCH/Orchestrator-8B-Q8_0-GGUF:Q8_0
>>> Hello 
<think>
Okay, the user said "Hello". I need to respond appropriately. Since they just greeted me, I should acknowledge their greeting and offer assistance. Let me make sure my response is friendly and 
open-ended. Maybe something like, "Hello! How can I assist you today?" That sounds good. I should keep it simple and inviting.
</think>

Hello! How can I assist you today? 😊

chat-template

{%- 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 message.content is string 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.content is string %}
        {%- set content = message.content %}
    {%- else %}
        {%- set content = '' %}
    {%- endif %}
    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
    {%- elif message.role == "assistant" %}
        {%- set reasoning_content = '' %}
        {%- if message.reasoning_content is string %}
            {%- set reasoning_content = message.reasoning_content %}
        {%- else %}
            {%- if '</think>' in content %}
                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
                {%- set content = content.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' }}
        {{- 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 %}

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo AXONVERTEX-AI-RESEARCH/Orchestrator-8B-Q8_0-GGUF --hf-file orchestrator-8b-q8_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo AXONVERTEX-AI-RESEARCH/Orchestrator-8B-Q8_0-GGUF --hf-file orchestrator-8b-q8_0.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo AXONVERTEX-AI-RESEARCH/Orchestrator-8B-Q8_0-GGUF --hf-file orchestrator-8b-q8_0.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo AXONVERTEX-AI-RESEARCH/Orchestrator-8B-Q8_0-GGUF --hf-file orchestrator-8b-q8_0.gguf -c 2048