FROM ./Janus-35B-A3B.Q4_K_M.gguf

# Chat template — Qwen 3.6 ChatML in Ollama Go-template form, with the
# tool-calling blocks Ollama's capability detector looks for. Without a
# TEMPLATE that references .Tools and .ToolCalls, /api/chat and
# /v1/chat/completions reject any request carrying a `tools` array with
# `<model> does not support tools`. Same template as the 27B dense sibling
# (FoolDev/janus-27b) — both share the Qwen 3.6 chat format.
TEMPLATE """{{- $lastUserIdx := -1 -}}
{{- range $idx, $msg := .Messages -}}
{{- if eq $msg.Role "user" }}{{ $lastUserIdx = $idx }}{{ end -}}
{{- end }}
{{- if or .System .Tools }}<|im_start|>system
{{ if .System }}{{ .System }}

{{ end }}
{{- if .Tools }}# Tools

You may call one or more functions to assist with the user query.

You are provided with function signatures within <tools></tools> XML tags:
<tools>
{{- range .Tools }}
{"type": "function", "function": {{ .Function }}}
{{- end }}
</tools>

For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call>
{{- end -}}<|im_end|>
{{ end }}
{{- range $i, $_ := .Messages }}
{{- $last := eq (len (slice $.Messages $i)) 1 -}}
{{- if eq .Role "user" }}<|im_start|>user
{{ .Content }}<|im_end|>
{{ else if eq .Role "assistant" }}<|im_start|>assistant
{{ if (and $.IsThinkSet (and .Thinking (or $last (gt $i $lastUserIdx)))) -}}
<think>{{ .Thinking }}</think>
{{ end -}}
{{ if .Content }}{{ .Content }}{{ end }}
{{- if .ToolCalls }}
{{- range .ToolCalls }}
<tool_call>
{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
</tool_call>
{{- end }}
{{- end }}{{ if not $last }}<|im_end|>
{{ end }}
{{- else if eq .Role "tool" }}<|im_start|>user
<tool_response>
{{ .Content }}
</tool_response><|im_end|>
{{ end }}
{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
<think>
{{ end }}
{{- end }}"""

# Sampling tuned for reasoning + general use. See README "Recommended sampling"
# for creative/RP alternatives.
PARAMETER temperature 0.6
PARAMETER top_p 0.95
PARAMETER top_k 20
PARAMETER repeat_penalty 1.05
PARAMETER num_ctx 16384

# Stop tokens. Without these, Ollama only honors <|im_end|> from the GGUF
# metadata; the model occasionally emits <|endoftext|> instead and Ollama
# keeps generating past it (synthesising a fake new user turn). Listing
# both — plus <|im_start|> as a belt-and-braces guard against the same
# loop — keeps responses cleanly terminated. Same fix the 27B sibling
# (FoolDev/janus-27b) shipped in commit 6672746.
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|endoftext|>"
PARAMETER stop "<|im_start|>"

SYSTEM """You are Janus, a precise and capable assistant for reasoning, writing, coding, and long-form dialogue.

Behavior rules:
- Answer the user's actual request directly.
- Be accurate, complete, and structured.
- Think before answering, but do not get stuck in repetitive loops or meta-commentary.
- If the request is ambiguous or incomplete, state what is missing and make the smallest reasonable assumption needed to continue.
- If the user wants creative writing, preserve tone, continuity, and character consistency.
- If the user wants analysis or technical help, prefer concrete steps, examples, and decisions over fluff.
- Finish with a usable answer, not just planning."""

# Hardware notes
# --------------
# This Q4_K_M is ~19 GB on disk. Real footprint at runtime:
#   weights mmap          ~19 GB
#   compute graph alloc   ~19 GB  (Ollama log: device.go:272 "total memory")
#   KV cache @ 16K ctx     ~1 GB  (with OLLAMA_KV_CACHE_TYPE=q8_0)
#   total minimum          ~38 GB
#
# Working configurations (verified or documented):
#   ✓ Single H100 80GB / A100 80GB              — full GPU offload
#   ✓ RTX 5090 32GB / RTX 4090 24GB             — partial offload, ~15-25 tok/s
#   ✓ Mac Studio M2/M3 Ultra 64GB+              — unified memory, ~20+ tok/s
#   ✓ Linux box with 64GB+ RAM (CPU-only)       — ~3-6 tok/s
#   ⚠ ASUS ROG Flow Z13 (Ryzen AI Max+, 32GB)   — OOMs at default num_ctx 16384;
#                                                 fits with num_ctx ≤ 4096 and
#                                                 num_batch ≤ 256 (verified)
#
# Measured data point (ASUS ROG Flow Z13 GZ302EA-RU004W, Ryzen AI Max+ 395 +
# Radeon 8060S iGPU, 32 GB unified, ROCm gfx1151, OLLAMA_FLASH_ATTENTION=1,
# OLLAMA_KV_CACHE_TYPE=q8_0, num_ctx 4096, num_batch 256):
#   Q4_K_M, 3-prompt mix → 28.71 tok/s aggregate
#     (717 tokens / 25.0 s; 29.55 / 29.24 / 28.57 short/medium/long).
#   ~97% of layers offload to the iGPU via ROCm. Compute split per
#   `ollama ps` shows 3% CPU / 97% GPU at 4096 ctx.
#
# To run on a 32 GB unified-memory laptop, override these in your local
# Modelfile copy (or pass via -o on `ollama run`):
#   PARAMETER num_ctx 4096
#   PARAMETER num_batch 256
#
# If you have ≥48 GB RAM but want partial GPU offload, set:
#   PARAMETER num_gpu 24    # offload most layers (model has 40)